wehub-resource-sync
5608 lines
833 KiB
Plaintext
5608 lines
833 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "ec486131",
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"metadata": {
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"papermill": {
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"duration": 0.00156,
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"end_time": "2026-06-13T03:33:36.775597+00:00",
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"exception": false,
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"start_time": "2026-06-13T03:33:36.774037+00:00",
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"status": "completed"
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}
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},
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"source": [
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"# Visual Diagnostics and Stationarity Testing\n",
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"\n",
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"**Docker image**: `ml4t`\n",
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"\n",
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"This notebook demonstrates the complete diagnostic workflow for financial\n",
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"time series: visual inspection, stationarity tests, autocorrelation analysis,\n",
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"and rolling diagnostic features.\n",
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"\n",
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"**Learning Objectives**:\n",
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"- Perform visual diagnostics (time series plot, ACF/PACF, Q-Q plot)\n",
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"- Test stationarity using ADF and KPSS with the joint decision matrix\n",
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"- Compute Ljung-Box test for residual autocorrelation\n",
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"- Build rolling ADF/KPSS statistics as time-varying features\n",
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"\n",
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"**Book Reference**: Chapter 9, Section 9.1 (Diagnostics and Stationarity Features)\n",
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"\n",
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"**Prerequisites**: None — this is the starting point for Ch9."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "cdf0428c",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2026-06-13T03:33:36.779176Z",
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"iopub.status.busy": "2026-06-13T03:33:36.779068Z",
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"iopub.status.idle": "2026-06-13T03:33:38.701490Z",
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"shell.execute_reply": "2026-06-13T03:33:38.701139Z"
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},
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"papermill": {
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"duration": 1.925236,
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"end_time": "2026-06-13T03:33:38.702429+00:00",
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"exception": false,
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"start_time": "2026-06-13T03:33:36.777193+00:00",
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"status": "completed"
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}
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},
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"outputs": [],
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"source": [
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"\"\"\"Visual Diagnostics and Stationarity Testing — the diagnostic workflow.\"\"\"\n",
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"\n",
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"import warnings\n",
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"\n",
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"warnings.filterwarnings(\"ignore\")\n",
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"\n",
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"from datetime import datetime\n",
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"\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import polars as pl\n",
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"from IPython.display import display\n",
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"from ml4t.diagnostic.evaluation.autocorrelation import analyze_autocorrelation\n",
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"from ml4t.diagnostic.evaluation.distribution import analyze_distribution\n",
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"from ml4t.diagnostic.evaluation.stationarity import analyze_stationarity\n",
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"from ml4t.diagnostic.evaluation.volatility import arch_lm_test\n",
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"from scipy.stats import norm, probplot\n",
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"from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
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"from statsmodels.stats.diagnostic import acorr_ljungbox\n",
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"from statsmodels.tsa.stattools import adfuller, kpss\n",
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"\n",
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"from data import load_etfs, load_macro"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "692fbde5",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2026-06-13T03:33:38.707561Z",
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"iopub.status.busy": "2026-06-13T03:33:38.707480Z",
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"iopub.status.idle": "2026-06-13T03:33:38.709034Z",
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"shell.execute_reply": "2026-06-13T03:33:38.708797Z"
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},
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"papermill": {
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"duration": 0.004635,
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"end_time": "2026-06-13T03:33:38.709429+00:00",
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"exception": false,
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"start_time": "2026-06-13T03:33:38.704794+00:00",
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"status": "completed"
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},
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"tags": [
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"parameters"
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]
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},
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"outputs": [],
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"source": [
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"# Production defaults — Papermill injects overrides for CI\n",
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"START_DATE = \"2000-01-01\"\n",
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"END_DATE = \"2024-12-31\"\n",
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"ROLLING_WINDOW = 252"
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]
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},
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{
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"cell_type": "markdown",
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"id": "19553b42",
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"metadata": {
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"papermill": {
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"duration": 0.001168,
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"end_time": "2026-06-13T03:33:38.712593+00:00",
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"exception": false,
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"start_time": "2026-06-13T03:33:38.711425+00:00",
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"status": "completed"
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}
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},
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"source": [
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"## Load Data\n",
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"\n",
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"SPY (S&P 500 ETF) for trending price series and VIX for a mean-reverting\n",
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"volatility series — contrasting stationarity behaviors."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "f9eba10d",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2026-06-13T03:33:38.715427Z",
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"iopub.status.busy": "2026-06-13T03:33:38.715365Z",
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"iopub.status.idle": "2026-06-13T03:33:38.755131Z",
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"shell.execute_reply": "2026-06-13T03:33:38.754763Z"
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},
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"papermill": {
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"duration": 0.041908,
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"end_time": "2026-06-13T03:33:38.755648+00:00",
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"exception": false,
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"start_time": "2026-06-13T03:33:38.713740+00:00",
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"status": "completed"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"S&P 500: 4,780 obs (2006-01-04 to 2024-12-31)\n",
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"VIX: 9,130 obs (2000-01-03 to 2024-12-31)\n"
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]
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}
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],
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"source": [
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"etfs = load_etfs(symbols=[\"SPY\"])\n",
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"sp500 = etfs.select([\"timestamp\", \"close\"]).rename({\"close\": \"value\"}).sort(\"timestamp\")\n",
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"\n",
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"macro = load_macro()\n",
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"vix = (\n",
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" macro.select([\"timestamp\", \"vixcls\"]).drop_nulls().rename({\"vixcls\": \"value\"}).sort(\"timestamp\")\n",
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")\n",
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"\n",
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"START = datetime.strptime(START_DATE, \"%Y-%m-%d\")\n",
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"END = datetime.strptime(END_DATE, \"%Y-%m-%d\")\n",
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"sp500 = sp500.filter((pl.col(\"timestamp\") >= START) & (pl.col(\"timestamp\") <= END))\n",
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"vix = vix.filter((pl.col(\"timestamp\") >= START) & (pl.col(\"timestamp\") <= END))\n",
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"\n",
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"# Add returns\n",
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"sp500 = sp500.with_columns(returns=pl.col(\"value\").pct_change() * 100).drop_nulls()\n",
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"\n",
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"sp500_pd = sp500.to_pandas().set_index(\"timestamp\")\n",
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"vix_pd = vix.to_pandas().set_index(\"timestamp\")\n",
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"returns = sp500_pd[\"returns\"]\n",
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"\n",
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"print(\n",
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" f\"S&P 500: {len(sp500_pd):,} obs ({sp500_pd.index.min().date()} to {sp500_pd.index.max().date()})\"\n",
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")\n",
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"print(f\"VIX: {len(vix_pd):,} obs ({vix_pd.index.min().date()} to {vix_pd.index.max().date()})\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c04e53a4",
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"metadata": {
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"papermill": {
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"duration": 0.001234,
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"end_time": "2026-06-13T03:33:38.758769+00:00",
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"exception": false,
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"start_time": "2026-06-13T03:33:38.757535+00:00",
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"status": "completed"
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}
|
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},
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"source": [
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"## Visual Inspection\n",
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"\n",
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"Start with four plots that reveal trend, volatility clustering, and\n",
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"distributional properties at a glance."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "852dd323",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2026-06-13T03:33:38.761646Z",
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"iopub.status.busy": "2026-06-13T03:33:38.761560Z",
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"iopub.status.idle": "2026-06-13T03:33:39.050086Z",
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"shell.execute_reply": "2026-06-13T03:33:39.049609Z"
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},
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"papermill": {
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"end_time": "2026-06-13T03:33:39.050575+00:00",
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"exception": false,
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"status": "completed"
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}
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},
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"outputs": [
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{
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"data": {
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",
|
|
"text/plain": [
|
|
"<Figure size 1400x800 with 4 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"fig, axes = plt.subplots(2, 2, figsize=(14, 8))\n",
|
|
"\n",
|
|
"ax = axes[0, 0]\n",
|
|
"ax.plot(sp500_pd.index, sp500_pd[\"value\"].values, linewidth=0.8)\n",
|
|
"ax.set_title(\"S&P 500 Index (Levels)\")\n",
|
|
"ax.set_ylabel(\"Index Value\")\n",
|
|
"\n",
|
|
"ax = axes[0, 1]\n",
|
|
"ax.plot(returns.index, returns.values, linewidth=0.5, alpha=0.8)\n",
|
|
"ax.axhline(0, color=\"red\", linestyle=\"--\", linewidth=0.5)\n",
|
|
"ax.set_title(\"S&P 500 Daily Returns (%)\")\n",
|
|
"ax.set_ylabel(\"Return (%)\")\n",
|
|
"\n",
|
|
"ax = axes[1, 0]\n",
|
|
"ax.plot(vix_pd.index, vix_pd[\"value\"].values, linewidth=0.8, color=\"orange\")\n",
|
|
"ax.axhline(20, color=\"red\", linestyle=\"--\", linewidth=0.5, label=\"VIX=20 threshold\")\n",
|
|
"ax.set_title(\"VIX Index\")\n",
|
|
"ax.set_ylabel(\"VIX Level\")\n",
|
|
"ax.legend()\n",
|
|
"\n",
|
|
"ax = axes[1, 1]\n",
|
|
"ax.hist(returns.values, bins=100, density=True, alpha=0.7, edgecolor=\"white\")\n",
|
|
"x = np.linspace(returns.min(), returns.max(), 100)\n",
|
|
"ax.plot(x, norm.pdf(x, returns.mean(), returns.std()), \"r-\", linewidth=2, label=\"Normal\")\n",
|
|
"ax.set_title(\"Return Distribution (Fat Tails)\")\n",
|
|
"ax.set_xlabel(\"Daily Return (%)\")\n",
|
|
"ax.set_ylabel(\"Density\")\n",
|
|
"ax.legend()\n",
|
|
"\n",
|
|
"plt.tight_layout()\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "c463186d",
|
|
"metadata": {
|
|
"lines_to_next_cell": 2,
|
|
"papermill": {
|
|
"duration": 0.00173,
|
|
"end_time": "2026-06-13T03:33:39.054058+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-13T03:33:39.052328+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"source": [
|
|
"## Stationarity Testing: ADF + KPSS Decision Matrix\n",
|
|
"\n",
|
|
"Using two tests with opposite null hypotheses provides robust conclusions:\n",
|
|
"\n",
|
|
"| ADF Result | KPSS Result | Conclusion |\n",
|
|
"|:-----------|:------------|:-----------|\n",
|
|
"| Reject H0 | Fail to reject H0 | Stationary (both agree) |\n",
|
|
"| Fail to reject H0 | Reject H0 | Non-stationary (both agree) |\n",
|
|
"| Reject H0 | Reject H0 | Trend-stationary |\n",
|
|
"| Fail to reject H0 | Fail to reject H0 | Inconclusive |"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 5,
|
|
"id": "4d0c4438",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2026-06-13T03:33:39.057670Z",
|
|
"iopub.status.busy": "2026-06-13T03:33:39.057591Z",
|
|
"iopub.status.idle": "2026-06-13T03:33:39.891900Z",
|
|
"shell.execute_reply": "2026-06-13T03:33:39.891478Z"
|
|
},
|
|
"papermill": {
|
|
"duration": 0.839656,
|
|
"end_time": "2026-06-13T03:33:39.895235+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-13T03:33:39.055579+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/tmp/ipykernel_1844253/1430242283.py:6: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is smaller than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_stat, kpss_pval, _, _ = kpss(series, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/1430242283.py:6: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_stat, kpss_pval, _, _ = kpss(series, regression=\"c\", nlags=\"auto\")\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/tmp/ipykernel_1844253/1430242283.py:6: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is smaller than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_stat, kpss_pval, _, _ = kpss(series, regression=\"c\", nlags=\"auto\")\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div>\n",
|
|
"<style scoped>\n",
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|
" vertical-align: middle;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe tbody tr th {\n",
|
|
" vertical-align: top;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe thead th {\n",
|
|
" text-align: right;\n",
|
|
" }\n",
|
|
"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>series</th>\n",
|
|
" <th>nobs</th>\n",
|
|
" <th>adf_stat</th>\n",
|
|
" <th>adf_pval</th>\n",
|
|
" <th>kpss_stat</th>\n",
|
|
" <th>kpss_pval</th>\n",
|
|
" <th>conclusion</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>S&P 500 Levels</td>\n",
|
|
" <td>4749</td>\n",
|
|
" <td>2.5856</td>\n",
|
|
" <td>0.9991</td>\n",
|
|
" <td>9.9166</td>\n",
|
|
" <td>0.01</td>\n",
|
|
" <td>Non-stationary (both agree)</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>S&P 500 Returns</td>\n",
|
|
" <td>4762</td>\n",
|
|
" <td>-17.3386</td>\n",
|
|
" <td>0.0000</td>\n",
|
|
" <td>0.1534</td>\n",
|
|
" <td>0.10</td>\n",
|
|
" <td>Stationary (both agree)</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>VIX Levels</td>\n",
|
|
" <td>9110</td>\n",
|
|
" <td>-5.9648</td>\n",
|
|
" <td>0.0000</td>\n",
|
|
" <td>0.8259</td>\n",
|
|
" <td>0.01</td>\n",
|
|
" <td>Trend-stationary</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" series nobs adf_stat adf_pval kpss_stat kpss_pval \\\n",
|
|
"0 S&P 500 Levels 4749 2.5856 0.9991 9.9166 0.01 \n",
|
|
"1 S&P 500 Returns 4762 -17.3386 0.0000 0.1534 0.10 \n",
|
|
"2 VIX Levels 9110 -5.9648 0.0000 0.8259 0.01 \n",
|
|
"\n",
|
|
" conclusion \n",
|
|
"0 Non-stationary (both agree) \n",
|
|
"1 Stationary (both agree) \n",
|
|
"2 Trend-stationary "
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"def run_stationarity_tests(series: pd.Series, name: str) -> dict:\n",
|
|
" \"\"\"Run ADF and KPSS tests with joint interpretation.\"\"\"\n",
|
|
" series = series.dropna()\n",
|
|
"\n",
|
|
" adf_stat, adf_pval, adf_lags, nobs, _, _ = adfuller(series, autolag=\"AIC\")\n",
|
|
" kpss_stat, kpss_pval, _, _ = kpss(series, regression=\"c\", nlags=\"auto\")\n",
|
|
"\n",
|
|
" if adf_pval < 0.05 and kpss_pval > 0.05:\n",
|
|
" conclusion = \"Stationary (both agree)\"\n",
|
|
" elif adf_pval > 0.05 and kpss_pval < 0.05:\n",
|
|
" conclusion = \"Non-stationary (both agree)\"\n",
|
|
" elif adf_pval < 0.05 and kpss_pval < 0.05:\n",
|
|
" conclusion = \"Trend-stationary\"\n",
|
|
" else:\n",
|
|
" conclusion = \"Inconclusive\"\n",
|
|
"\n",
|
|
" return {\n",
|
|
" \"series\": name,\n",
|
|
" \"nobs\": nobs,\n",
|
|
" \"adf_stat\": round(adf_stat, 4),\n",
|
|
" \"adf_pval\": round(adf_pval, 4),\n",
|
|
" \"kpss_stat\": round(kpss_stat, 4),\n",
|
|
" \"kpss_pval\": round(kpss_pval, 4),\n",
|
|
" \"conclusion\": conclusion,\n",
|
|
" }\n",
|
|
"\n",
|
|
"\n",
|
|
"results = [\n",
|
|
" run_stationarity_tests(sp500_pd[\"value\"], \"S&P 500 Levels\"),\n",
|
|
" run_stationarity_tests(returns, \"S&P 500 Returns\"),\n",
|
|
" run_stationarity_tests(vix_pd[\"value\"], \"VIX Levels\"),\n",
|
|
"]\n",
|
|
"\n",
|
|
"results_df = pd.DataFrame(results)\n",
|
|
"display(results_df)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "098bad6e",
|
|
"metadata": {
|
|
"papermill": {
|
|
"duration": 0.005008,
|
|
"end_time": "2026-06-13T03:33:39.905344+00:00",
|
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"exception": false,
|
|
"start_time": "2026-06-13T03:33:39.900336+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"source": [
|
|
"**Finding**: Prices are non-stationary (unit root); returns are stationary.\n",
|
|
"VIX is mean-reverting but may show trend-stationarity due to structural shifts."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "73ceac64",
|
|
"metadata": {
|
|
"papermill": {
|
|
"duration": 0.002872,
|
|
"end_time": "2026-06-13T03:33:39.912289+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-13T03:33:39.909417+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"source": [
|
|
"### ml4t-diagnostic: Consensus Stationarity Analysis\n",
|
|
"\n",
|
|
"The manual approach above requires running two separate tests and interpreting\n",
|
|
"a decision matrix. `analyze_stationarity()` runs ADF, KPSS, and Phillips-Perron\n",
|
|
"in one call and returns a consensus classification with agreement score."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"id": "79e45682",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2026-06-13T03:33:39.918631Z",
|
|
"iopub.status.busy": "2026-06-13T03:33:39.918463Z",
|
|
"iopub.status.idle": "2026-06-13T03:33:40.541446Z",
|
|
"shell.execute_reply": "2026-06-13T03:33:40.541113Z"
|
|
},
|
|
"papermill": {
|
|
"duration": 0.630443,
|
|
"end_time": "2026-06-13T03:33:40.545301+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-13T03:33:39.914858+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"2026-06-12 23:33:39,939 - ml4t.diagnostic.evaluation.stationarity.analysis - INFO - Running comprehensive stationarity analysis (n_obs=4780 tests=['adf', 'kpss', 'pp'] alpha=0.05)\n"
|
|
]
|
|
},
|
|
{
|
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"name": "stderr",
|
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"output_type": "stream",
|
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"text": [
|
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"2026-06-12 23:33:39,941 - ml4t.diagnostic.evaluation.stationarity.augmented_dickey_fuller - INFO - Running ADF test (n_obs=4780 maxlag=None regression=c autolag=AIC)\n"
|
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]
|
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},
|
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{
|
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"name": "stderr",
|
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"output_type": "stream",
|
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"text": [
|
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"2026-06-12 23:33:40,066 - ml4t.diagnostic.evaluation.stationarity.augmented_dickey_fuller - INFO - ADF test completed (statistic=2.585597573520157 p_value=0.9990723520827669 lags_used=30 n_obs=4749 stationary=False)\n"
|
|
]
|
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},
|
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{
|
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"name": "stderr",
|
|
"output_type": "stream",
|
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"text": [
|
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"2026-06-12 23:33:40,068 - ml4t.diagnostic.evaluation.stationarity.analysis - INFO - ADF test completed (stationary=False)\n"
|
|
]
|
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},
|
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{
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"name": "stderr",
|
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"output_type": "stream",
|
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"text": [
|
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"2026-06-12 23:33:40,068 - ml4t.diagnostic.evaluation.stationarity.kpss_test - INFO - Running KPSS test (n_obs=4780 regression=c nlags=auto)\n"
|
|
]
|
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},
|
|
{
|
|
"name": "stderr",
|
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"output_type": "stream",
|
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"text": [
|
|
".venv/lib/python3.14/site-packages/ml4t/diagnostic/evaluation/stationarity/kpss_test.py:269: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is smaller than the p-value returned.\n",
|
|
"\n",
|
|
" result = kpss(arr, regression=regression, nlags=nlags_param)\n",
|
|
"2026-06-12 23:33:40,070 - ml4t.diagnostic.evaluation.stationarity.kpss_test - INFO - KPSS test completed (statistic=9.916610728355257 p_value=0.01 lags_used=42 n_obs=4780 stationary=False)\n"
|
|
]
|
|
},
|
|
{
|
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"name": "stderr",
|
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"output_type": "stream",
|
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"text": [
|
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"2026-06-12 23:33:40,070 - ml4t.diagnostic.evaluation.stationarity.analysis - INFO - KPSS test completed (stationary=False)\n"
|
|
]
|
|
},
|
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{
|
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"name": "stderr",
|
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"output_type": "stream",
|
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"text": [
|
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"2026-06-12 23:33:40,071 - ml4t.diagnostic.evaluation.stationarity.phillips_perron - INFO - Running PP test (n_obs=4780 lags=None regression=c test_type=tau)\n"
|
|
]
|
|
},
|
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{
|
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"name": "stderr",
|
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"output_type": "stream",
|
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"text": [
|
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"2026-06-12 23:33:40,074 - ml4t.diagnostic.evaluation.stationarity.phillips_perron - INFO - PP test completed (statistic=2.227920453935083 p_value=0.9989047031995539 lags_used=32 n_obs=4779 stationary=False)\n"
|
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]
|
|
},
|
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{
|
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"name": "stderr",
|
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"output_type": "stream",
|
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"text": [
|
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"2026-06-12 23:33:40,075 - ml4t.diagnostic.evaluation.stationarity.analysis - INFO - PP test completed (stationary=False)\n"
|
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
|
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"2026-06-12 23:33:40,075 - ml4t.diagnostic.evaluation.stationarity.analysis - INFO - Stationarity analysis completed (n_tests_run=3 consensus=strong_nonstationary agreement=1.0)\n"
|
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]
|
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},
|
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{
|
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"name": "stderr",
|
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"output_type": "stream",
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"text": [
|
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"2026-06-12 23:33:40,076 - ml4t.diagnostic.evaluation.stationarity.analysis - INFO - Running comprehensive stationarity analysis (n_obs=4780 tests=['adf', 'kpss', 'pp'] alpha=0.05)\n"
|
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]
|
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},
|
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{
|
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"name": "stderr",
|
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"output_type": "stream",
|
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"text": [
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"2026-06-12 23:33:40,076 - ml4t.diagnostic.evaluation.stationarity.augmented_dickey_fuller - INFO - Running ADF test (n_obs=4780 maxlag=None regression=c autolag=AIC)\n"
|
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]
|
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},
|
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{
|
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"name": "stderr",
|
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"output_type": "stream",
|
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"text": [
|
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"2026-06-12 23:33:40,152 - ml4t.diagnostic.evaluation.stationarity.augmented_dickey_fuller - INFO - ADF test completed (statistic=-17.338557658766298 p_value=5.3570075992390524e-30 lags_used=17 n_obs=4762 stationary=True)\n"
|
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]
|
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},
|
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{
|
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"name": "stderr",
|
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"output_type": "stream",
|
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"text": [
|
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"2026-06-12 23:33:40,153 - ml4t.diagnostic.evaluation.stationarity.analysis - INFO - ADF test completed (stationary=True)\n"
|
|
]
|
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},
|
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{
|
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"name": "stderr",
|
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"output_type": "stream",
|
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"text": [
|
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"2026-06-12 23:33:40,153 - ml4t.diagnostic.evaluation.stationarity.kpss_test - INFO - Running KPSS test (n_obs=4780 regression=c nlags=auto)\n"
|
|
]
|
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},
|
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{
|
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"name": "stderr",
|
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"output_type": "stream",
|
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"text": [
|
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".venv/lib/python3.14/site-packages/ml4t/diagnostic/evaluation/stationarity/kpss_test.py:269: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" result = kpss(arr, regression=regression, nlags=nlags_param)\n",
|
|
"2026-06-12 23:33:40,153 - ml4t.diagnostic.evaluation.stationarity.kpss_test - INFO - KPSS test completed (statistic=0.1534042107270784 p_value=0.1 lags_used=28 n_obs=4780 stationary=True)\n"
|
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]
|
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},
|
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{
|
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"name": "stderr",
|
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"output_type": "stream",
|
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"text": [
|
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"2026-06-12 23:33:40,153 - ml4t.diagnostic.evaluation.stationarity.analysis - INFO - KPSS test completed (stationary=True)\n"
|
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]
|
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},
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{
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"name": "stderr",
|
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"output_type": "stream",
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"text": [
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"2026-06-12 23:33:40,154 - ml4t.diagnostic.evaluation.stationarity.phillips_perron - INFO - Running PP test (n_obs=4780 lags=None regression=c test_type=tau)\n"
|
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]
|
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},
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{
|
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"name": "stderr",
|
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"output_type": "stream",
|
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"text": [
|
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"2026-06-12 23:33:40,157 - ml4t.diagnostic.evaluation.stationarity.phillips_perron - INFO - PP test completed (statistic=-77.91291156208489 p_value=0.0 lags_used=32 n_obs=4779 stationary=True)\n"
|
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]
|
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},
|
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{
|
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"name": "stderr",
|
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"output_type": "stream",
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"text": [
|
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"2026-06-12 23:33:40,157 - ml4t.diagnostic.evaluation.stationarity.analysis - INFO - PP test completed (stationary=True)\n"
|
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]
|
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},
|
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{
|
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"name": "stderr",
|
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"output_type": "stream",
|
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"text": [
|
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"2026-06-12 23:33:40,158 - ml4t.diagnostic.evaluation.stationarity.analysis - INFO - Stationarity analysis completed (n_tests_run=3 consensus=strong_stationary agreement=1.0)\n"
|
|
]
|
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},
|
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{
|
|
"name": "stderr",
|
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"output_type": "stream",
|
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"text": [
|
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"2026-06-12 23:33:40,158 - ml4t.diagnostic.evaluation.stationarity.analysis - INFO - Running comprehensive stationarity analysis (n_obs=9130 tests=['adf', 'kpss', 'pp'] alpha=0.05)\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
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"output_type": "stream",
|
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"text": [
|
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"2026-06-12 23:33:40,158 - ml4t.diagnostic.evaluation.stationarity.augmented_dickey_fuller - INFO - Running ADF test (n_obs=9130 maxlag=None regression=c autolag=AIC)\n"
|
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]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"S&P 500 Levels : consensus=strong_nonstationary, agreement=1.00\n",
|
|
"S&P 500 Returns : consensus=strong_stationary, agreement=1.00\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
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"text": [
|
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"2026-06-12 23:33:40,532 - ml4t.diagnostic.evaluation.stationarity.augmented_dickey_fuller - INFO - ADF test completed (statistic=-5.964795457103011 p_value=1.9995698487860696e-07 lags_used=19 n_obs=9110 stationary=True)\n"
|
|
]
|
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},
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{
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"name": "stderr",
|
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"output_type": "stream",
|
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"text": [
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"2026-06-12 23:33:40,532 - ml4t.diagnostic.evaluation.stationarity.analysis - INFO - ADF test completed (stationary=True)\n"
|
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]
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},
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{
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"name": "stderr",
|
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"output_type": "stream",
|
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"text": [
|
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"2026-06-12 23:33:40,533 - ml4t.diagnostic.evaluation.stationarity.kpss_test - INFO - Running KPSS test (n_obs=9130 regression=c nlags=auto)\n"
|
|
]
|
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},
|
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{
|
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"name": "stderr",
|
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"output_type": "stream",
|
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"text": [
|
|
".venv/lib/python3.14/site-packages/ml4t/diagnostic/evaluation/stationarity/kpss_test.py:269: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is smaller than the p-value returned.\n",
|
|
"\n",
|
|
" result = kpss(arr, regression=regression, nlags=nlags_param)\n",
|
|
"2026-06-12 23:33:40,533 - ml4t.diagnostic.evaluation.stationarity.kpss_test - INFO - KPSS test completed (statistic=0.825856005437208 p_value=0.01 lags_used=57 n_obs=9130 stationary=False)\n"
|
|
]
|
|
},
|
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{
|
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"name": "stderr",
|
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"output_type": "stream",
|
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"text": [
|
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"2026-06-12 23:33:40,533 - ml4t.diagnostic.evaluation.stationarity.analysis - INFO - KPSS test completed (stationary=False)\n"
|
|
]
|
|
},
|
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{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
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"text": [
|
|
"2026-06-12 23:33:40,534 - ml4t.diagnostic.evaluation.stationarity.phillips_perron - INFO - Running PP test (n_obs=9130 lags=None regression=c test_type=tau)\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"2026-06-12 23:33:40,538 - ml4t.diagnostic.evaluation.stationarity.phillips_perron - INFO - PP test completed (statistic=-7.3259357428906196 p_value=1.1610671212864292e-10 lags_used=38 n_obs=9129 stationary=True)\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
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"text": [
|
|
"2026-06-12 23:33:40,539 - ml4t.diagnostic.evaluation.stationarity.analysis - INFO - PP test completed (stationary=True)\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
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"text": [
|
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"2026-06-12 23:33:40,539 - ml4t.diagnostic.evaluation.stationarity.analysis - INFO - Stationarity analysis completed (n_tests_run=3 consensus=likely_stationary agreement=0.6666666666666666)\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"VIX Levels : consensus=likely_stationary, agreement=0.67\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"for name, series in [\n",
|
|
" (\"S&P 500 Levels\", sp500_pd[\"value\"]),\n",
|
|
" (\"S&P 500 Returns\", returns),\n",
|
|
" (\"VIX Levels\", vix_pd[\"value\"]),\n",
|
|
"]:\n",
|
|
" result = analyze_stationarity(series.dropna().values)\n",
|
|
" print(f\"{name:20s}: consensus={result.consensus}, agreement={result.agreement_score:.2f}\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "e00bded0",
|
|
"metadata": {
|
|
"papermill": {
|
|
"duration": 0.007145,
|
|
"end_time": "2026-06-13T03:33:40.559871+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-13T03:33:40.552726+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"source": [
|
|
"The three-test consensus (ADF + KPSS + Phillips-Perron) is more robust than\n",
|
|
"the two-test decision matrix. The agreement score quantifies how strongly\n",
|
|
"the tests agree — 1.0 means unanimous, below 0.5 is inconclusive."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "7f2acea1",
|
|
"metadata": {
|
|
"lines_to_next_cell": 2,
|
|
"papermill": {
|
|
"duration": 0.007091,
|
|
"end_time": "2026-06-13T03:33:40.577123+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-13T03:33:40.570032+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"source": [
|
|
"## Autocorrelation Analysis\n",
|
|
"\n",
|
|
"ACF/PACF plots reveal the lag structure. Key patterns:\n",
|
|
"- Slow ACF decay → non-stationarity\n",
|
|
"- Sharp PACF cutoff → AR process (cutoff at lag $p$)\n",
|
|
"- Sharp ACF cutoff → MA process (cutoff at lag $q$)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"id": "5c5480f4",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2026-06-13T03:33:40.591463Z",
|
|
"iopub.status.busy": "2026-06-13T03:33:40.591235Z",
|
|
"iopub.status.idle": "2026-06-13T03:33:40.923806Z",
|
|
"shell.execute_reply": "2026-06-13T03:33:40.923307Z"
|
|
},
|
|
"papermill": {
|
|
"duration": 0.337063,
|
|
"end_time": "2026-06-13T03:33:40.924281+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-13T03:33:40.587218+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 1400x800 with 4 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"def plot_correlogram(series: pd.Series, title: str, lags: int = 40):\n",
|
|
" \"\"\"Create diagnostic correlogram: time series, Q-Q, ACF, PACF.\"\"\"\n",
|
|
" series = series.dropna()\n",
|
|
" fig, axes = plt.subplots(2, 2, figsize=(14, 8))\n",
|
|
"\n",
|
|
" ax = axes[0, 0]\n",
|
|
" ax.plot(series.index, series.values, linewidth=0.5, alpha=0.7, label=\"Series\")\n",
|
|
" rolling_mean = series.rolling(21).mean()\n",
|
|
" ax.plot(series.index, rolling_mean.values, linewidth=1.5, color=\"red\", label=\"21-day MA\")\n",
|
|
" ax.set_title(\"Time Series with Rolling Mean\")\n",
|
|
" ax.legend()\n",
|
|
"\n",
|
|
" # Stats annotation\n",
|
|
" adf_pval = adfuller(series, autolag=\"AIC\")[1]\n",
|
|
" ljung = acorr_ljungbox(series, lags=[10], return_df=True)\n",
|
|
" lb_pval = ljung[\"lb_pvalue\"].values[0]\n",
|
|
" ax.text(\n",
|
|
" 0.02,\n",
|
|
" 0.95,\n",
|
|
" f\"ADF p={adf_pval:.4f}\\nLjung-Box(10) p={lb_pval:.4f}\",\n",
|
|
" transform=ax.transAxes,\n",
|
|
" va=\"top\",\n",
|
|
" fontsize=9,\n",
|
|
" bbox=dict(boxstyle=\"round\", facecolor=\"wheat\", alpha=0.5),\n",
|
|
" )\n",
|
|
"\n",
|
|
" ax = axes[0, 1]\n",
|
|
" probplot(series, dist=\"norm\", plot=ax)\n",
|
|
" ax.set_title(\"Q-Q Plot (Normal)\")\n",
|
|
" skew_val = series.skew()\n",
|
|
" kurt_val = series.kurtosis()\n",
|
|
" ax.text(\n",
|
|
" 0.02,\n",
|
|
" 0.95,\n",
|
|
" f\"Skew: {skew_val:.2f}\\nKurtosis: {kurt_val:.2f}\",\n",
|
|
" transform=ax.transAxes,\n",
|
|
" va=\"top\",\n",
|
|
" fontsize=9,\n",
|
|
" bbox=dict(boxstyle=\"round\", facecolor=\"wheat\", alpha=0.5),\n",
|
|
" )\n",
|
|
"\n",
|
|
" ax = axes[1, 0]\n",
|
|
" plot_acf(series, lags=lags, zero=False, ax=ax)\n",
|
|
" ax.set_title(\"ACF\")\n",
|
|
"\n",
|
|
" ax = axes[1, 1]\n",
|
|
" plot_pacf(series, lags=lags, zero=False, ax=ax, method=\"ywm\")\n",
|
|
" ax.set_title(\"PACF\")\n",
|
|
"\n",
|
|
" fig.suptitle(title, fontsize=14, fontweight=\"bold\")\n",
|
|
" plt.tight_layout()\n",
|
|
" return fig\n",
|
|
"\n",
|
|
"\n",
|
|
"fig = plot_correlogram(returns, \"S&P 500 Daily Returns — Correlogram\")\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "e2347776",
|
|
"metadata": {
|
|
"papermill": {
|
|
"duration": 0.002396,
|
|
"end_time": "2026-06-13T03:33:40.929683+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-13T03:33:40.927287+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"source": [
|
|
"**Observations**: ACF and PACF stay close to zero at every lag, so any linear\n",
|
|
"dependence in raw returns is small in magnitude — even where formal tests\n",
|
|
"(below) detect it given the long sample. The Q-Q plot fans out at both ends,\n",
|
|
"consistent with fat tails and excess kurtosis well above the Gaussian baseline."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "be225ea2",
|
|
"metadata": {
|
|
"papermill": {
|
|
"duration": 0.002341,
|
|
"end_time": "2026-06-13T03:33:40.934426+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-13T03:33:40.932085+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"source": [
|
|
"### ml4t-diagnostic: Autocorrelation Analysis with ARIMA Suggestion\n",
|
|
"\n",
|
|
"`analyze_autocorrelation()` examines the ACF/PACF patterns and suggests\n",
|
|
"ARIMA orders — useful before fitting time series models in NB07."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"id": "007abe0b",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2026-06-13T03:33:40.939826Z",
|
|
"iopub.status.busy": "2026-06-13T03:33:40.939736Z",
|
|
"iopub.status.idle": "2026-06-13T03:33:40.948021Z",
|
|
"shell.execute_reply": "2026-06-13T03:33:40.947732Z"
|
|
},
|
|
"papermill": {
|
|
"duration": 0.011498,
|
|
"end_time": "2026-06-13T03:33:40.948270+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-13T03:33:40.936772+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"2026-06-12 23:33:40,940 - ml4t.diagnostic.evaluation.autocorrelation - INFO - Starting autocorrelation analysis\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"2026-06-12 23:33:40,942 - ml4t.diagnostic.evaluation.autocorrelation - INFO - ACF computed (n_obs=4780 nlags=36 significant=10)\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"2026-06-12 23:33:40,946 - ml4t.diagnostic.evaluation.autocorrelation - INFO - PACF computed (n_obs=4780 nlags=36 significant=11)\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"2026-06-12 23:33:40,946 - ml4t.diagnostic.evaluation.autocorrelation - INFO - Autocorrelation analysis completed (arima_order=(2, 0, 1) white_noise=False)\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"=== ml4t-diagnostic: Autocorrelation Analysis ===\n",
|
|
"Suggested ARIMA order: (2, 0, 1)\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"acf_result = analyze_autocorrelation(returns.dropna().values)\n",
|
|
"print(\"=== ml4t-diagnostic: Autocorrelation Analysis ===\")\n",
|
|
"print(f\"Suggested ARIMA order: {acf_result.suggested_arima_order}\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "a7e5b32b",
|
|
"metadata": {
|
|
"papermill": {
|
|
"duration": 0.002481,
|
|
"end_time": "2026-06-13T03:33:40.953292+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-13T03:33:40.950811+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"source": [
|
|
"## Ljung-Box Test\n",
|
|
"\n",
|
|
"Formal test for whether the first $m$ autocorrelations are jointly zero.\n",
|
|
"Useful for checking model residuals."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"id": "b07e02cb",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2026-06-13T03:33:40.958810Z",
|
|
"iopub.status.busy": "2026-06-13T03:33:40.958721Z",
|
|
"iopub.status.idle": "2026-06-13T03:33:40.968084Z",
|
|
"shell.execute_reply": "2026-06-13T03:33:40.967802Z"
|
|
},
|
|
"papermill": {
|
|
"duration": 0.012775,
|
|
"end_time": "2026-06-13T03:33:40.968478+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-13T03:33:40.955703+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Returns: autocorrelation detected\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div>\n",
|
|
"<style scoped>\n",
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
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" vertical-align: middle;\n",
|
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" }\n",
|
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"\n",
|
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" .dataframe tbody tr th {\n",
|
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" vertical-align: top;\n",
|
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" }\n",
|
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"\n",
|
|
" .dataframe thead th {\n",
|
|
" text-align: right;\n",
|
|
" }\n",
|
|
"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>lb_stat</th>\n",
|
|
" <th>lb_pvalue</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>5</th>\n",
|
|
" <td>59.861214</td>\n",
|
|
" <td>1.298427e-11</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>10</th>\n",
|
|
" <td>97.385513</td>\n",
|
|
" <td>1.815718e-16</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>20</th>\n",
|
|
" <td>149.584984</td>\n",
|
|
" <td>7.547474e-22</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>40</th>\n",
|
|
" <td>179.556515</td>\n",
|
|
" <td>1.369741e-19</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" lb_stat lb_pvalue\n",
|
|
"5 59.861214 1.298427e-11\n",
|
|
"10 97.385513 1.815718e-16\n",
|
|
"20 149.584984 7.547474e-22\n",
|
|
"40 179.556515 1.369741e-19"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Squared returns: ARCH effects present\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div>\n",
|
|
"<style scoped>\n",
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|
" vertical-align: middle;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe tbody tr th {\n",
|
|
" vertical-align: top;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe thead th {\n",
|
|
" text-align: right;\n",
|
|
" }\n",
|
|
"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>lb_stat</th>\n",
|
|
" <th>lb_pvalue</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>5</th>\n",
|
|
" <td>2397.216257</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>10</th>\n",
|
|
" <td>4095.474843</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>20</th>\n",
|
|
" <td>5971.346361</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>40</th>\n",
|
|
" <td>7427.790338</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" lb_stat lb_pvalue\n",
|
|
"5 2397.216257 0.0\n",
|
|
"10 4095.474843 0.0\n",
|
|
"20 5971.346361 0.0\n",
|
|
"40 7427.790338 0.0"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"lb_results = acorr_ljungbox(returns, lags=[5, 10, 20, 40], return_df=True)\n",
|
|
"lb_returns_reject = (lb_results[\"lb_pvalue\"] < 0.05).any()\n",
|
|
"print(\n",
|
|
" f\"Returns: {'autocorrelation detected' if lb_returns_reject else 'no significant autocorrelation'}\"\n",
|
|
")\n",
|
|
"display(lb_results)\n",
|
|
"\n",
|
|
"# Check squared returns (volatility clustering)\n",
|
|
"lb_sq = acorr_ljungbox(returns**2, lags=[5, 10, 20, 40], return_df=True)\n",
|
|
"lb_sq_reject = (lb_sq[\"lb_pvalue\"] < 0.05).any()\n",
|
|
"print(f\"Squared returns: {'ARCH effects present' if lb_sq_reject else 'no ARCH effects'}\")\n",
|
|
"display(lb_sq)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "01098ee0",
|
|
"metadata": {
|
|
"papermill": {
|
|
"duration": 0.002553,
|
|
"end_time": "2026-06-13T03:33:40.973751+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-13T03:33:40.971198+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"source": [
|
|
"**Finding**: Highly significant Ljung-Box statistics on squared returns confirm\n",
|
|
"ARCH effects — volatility clusters in time. This motivates the GARCH models\n",
|
|
"developed in `08_garch_volatility`."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "3206ca8d",
|
|
"metadata": {
|
|
"papermill": {
|
|
"duration": 0.002509,
|
|
"end_time": "2026-06-13T03:33:40.978809+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-13T03:33:40.976300+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"source": [
|
|
"## Rolling Stationarity Features\n",
|
|
"\n",
|
|
"Stationarity is not a fixed property — it can change over time. Rolling\n",
|
|
"ADF/KPSS statistics become time-varying features that detect when\n",
|
|
"relationships break down (e.g., cointegration weakening)."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"id": "9a2434dd",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2026-06-13T03:33:40.984592Z",
|
|
"iopub.status.busy": "2026-06-13T03:33:40.984494Z",
|
|
"iopub.status.idle": "2026-06-13T03:33:43.068673Z",
|
|
"shell.execute_reply": "2026-06-13T03:33:43.068114Z"
|
|
},
|
|
"papermill": {
|
|
"duration": 2.087843,
|
|
"end_time": "2026-06-13T03:33:43.069209+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-13T03:33:40.981366+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
"/tmp/ipykernel_1844253/2245296499.py:10: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
|
|
"look-up table. The actual p-value is greater than the p-value returned.\n",
|
|
"\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"WINDOW = ROLLING_WINDOW # From parameters cell\n",
|
|
"STEP = 5 # Compute every 5 days\n",
|
|
"\n",
|
|
"rolling_stats = []\n",
|
|
"\n",
|
|
"for end in range(WINDOW, len(returns), STEP):\n",
|
|
" window_data = returns.iloc[end - WINDOW : end]\n",
|
|
" try:\n",
|
|
" adf_result = adfuller(window_data, autolag=\"AIC\")\n",
|
|
" kpss_result = kpss(window_data, regression=\"c\", nlags=\"auto\")\n",
|
|
" adf_stat = adf_result[0]\n",
|
|
" kpss_stat = kpss_result[0]\n",
|
|
"\n",
|
|
" # Decision: both agree on stationary?\n",
|
|
" adf_reject = adf_result[1] < 0.05\n",
|
|
" kpss_not_reject = kpss_result[1] > 0.05\n",
|
|
" stationary = int(adf_reject and kpss_not_reject)\n",
|
|
"\n",
|
|
" rolling_stats.append(\n",
|
|
" {\n",
|
|
" \"timestamp\": returns.index[end],\n",
|
|
" \"adf_statistic\": adf_stat,\n",
|
|
" \"kpss_statistic\": kpss_stat,\n",
|
|
" \"stationarity_regime\": stationary,\n",
|
|
" }\n",
|
|
" )\n",
|
|
" except Exception:\n",
|
|
" continue\n",
|
|
"\n",
|
|
"rolling_df = pd.DataFrame(rolling_stats).set_index(\"timestamp\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"id": "d8e4354b",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2026-06-13T03:33:43.078406Z",
|
|
"iopub.status.busy": "2026-06-13T03:33:43.078266Z",
|
|
"iopub.status.idle": "2026-06-13T03:33:43.305233Z",
|
|
"shell.execute_reply": "2026-06-13T03:33:43.304677Z"
|
|
},
|
|
"papermill": {
|
|
"duration": 0.232129,
|
|
"end_time": "2026-06-13T03:33:43.305764+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-13T03:33:43.073635+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 1400x900 with 3 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"fig, axes = plt.subplots(3, 1, figsize=(14, 9), sharex=True)\n",
|
|
"\n",
|
|
"ax = axes[0]\n",
|
|
"ax.plot(rolling_df.index, rolling_df[\"adf_statistic\"], linewidth=0.8)\n",
|
|
"ax.axhline(-2.86, color=\"red\", linestyle=\"--\", linewidth=0.5, label=\"5% critical\")\n",
|
|
"ax.set_title(\"Rolling ADF Statistic (252-Day Window)\")\n",
|
|
"ax.set_ylabel(\"ADF Statistic\")\n",
|
|
"ax.legend()\n",
|
|
"\n",
|
|
"ax = axes[1]\n",
|
|
"ax.plot(rolling_df.index, rolling_df[\"kpss_statistic\"], linewidth=0.8, color=\"orange\")\n",
|
|
"ax.axhline(0.463, color=\"red\", linestyle=\"--\", linewidth=0.5, label=\"5% critical\")\n",
|
|
"ax.set_title(\"Rolling KPSS Statistic (252-Day Window)\")\n",
|
|
"ax.set_ylabel(\"KPSS Statistic\")\n",
|
|
"ax.legend()\n",
|
|
"\n",
|
|
"ax = axes[2]\n",
|
|
"ax.fill_between(rolling_df.index, 0, rolling_df[\"stationarity_regime\"], alpha=0.5, color=\"green\")\n",
|
|
"ax.set_title(\"Stationarity Regime (1 = Stationary by Both Tests)\")\n",
|
|
"ax.set_ylabel(\"Regime\")\n",
|
|
"\n",
|
|
"plt.tight_layout()\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "808c9d97",
|
|
"metadata": {
|
|
"papermill": {
|
|
"duration": 0.005032,
|
|
"end_time": "2026-06-13T03:33:43.316113+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-13T03:33:43.311081+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"source": [
|
|
"## Distribution Analysis\n",
|
|
"\n",
|
|
"Beyond stationarity and autocorrelation, the distribution of returns matters\n",
|
|
"for model selection: fat tails affect VaR, skewness affects directional bets,\n",
|
|
"and ARCH effects motivate GARCH models (NB08)."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"id": "0ba28ffa",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2026-06-13T03:33:43.326456Z",
|
|
"iopub.status.busy": "2026-06-13T03:33:43.326358Z",
|
|
"iopub.status.idle": "2026-06-13T03:33:43.337891Z",
|
|
"shell.execute_reply": "2026-06-13T03:33:43.337601Z"
|
|
},
|
|
"papermill": {
|
|
"duration": 0.017228,
|
|
"end_time": "2026-06-13T03:33:43.338191+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-13T03:33:43.320963+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"2026-06-12 23:33:43,327 - ml4t.diagnostic.evaluation.distribution - INFO - Starting comprehensive distribution analysis (compute_tails=True alpha=0.05)\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"2026-06-12 23:33:43,327 - ml4t.diagnostic.evaluation.distribution.moments - INFO - Computing distribution moments (n_obs=4780)\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"2026-06-12 23:33:43,327 - ml4t.diagnostic.evaluation.distribution.moments - INFO - Computing distribution moments (n_obs=4780)\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"2026-06-12 23:33:43,329 - ml4t.diagnostic.evaluation.distribution.moments - INFO - Moments computed (skewness=-0.08096319832510852 excess_kurtosis=14.564166268785257 skewness_sig=True excess_kurtosis_sig=True)\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"2026-06-12 23:33:43,329 - ml4t.diagnostic.evaluation.distribution.moments - INFO - Moments computed (skewness=-0.08096319832510852 excess_kurtosis=14.564166268785257 skewness_sig=True excess_kurtosis_sig=True)\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"2026-06-12 23:33:43,329 - ml4t.diagnostic.evaluation.distribution.tests - INFO - Running Jarque-Bera test (n_obs=4780 alpha=0.05)\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"2026-06-12 23:33:43,329 - ml4t.diagnostic.evaluation.distribution.tests - INFO - Running Jarque-Bera test (n_obs=4780 alpha=0.05)\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"2026-06-12 23:33:43,331 - ml4t.diagnostic.evaluation.distribution.tests - INFO - Jarque-Bera test completed (statistic=42155.86249760509 p_value=0.0 is_normal=False)\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"2026-06-12 23:33:43,331 - ml4t.diagnostic.evaluation.distribution.tests - INFO - Jarque-Bera test completed (statistic=42155.86249760509 p_value=0.0 is_normal=False)\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"2026-06-12 23:33:43,331 - ml4t.diagnostic.evaluation.distribution.tests - INFO - Running Shapiro-Wilk test (n_obs=4780 alpha=0.05)\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"2026-06-12 23:33:43,331 - ml4t.diagnostic.evaluation.distribution.tests - INFO - Running Shapiro-Wilk test (n_obs=4780 alpha=0.05)\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"2026-06-12 23:33:43,332 - ml4t.diagnostic.evaluation.distribution.tests - INFO - Shapiro-Wilk test completed (statistic=0.8713476777827477 p_value=1.0647105451113958e-52 is_normal=False)\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"2026-06-12 23:33:43,332 - ml4t.diagnostic.evaluation.distribution.tests - INFO - Shapiro-Wilk test completed (statistic=0.8713476777827477 p_value=1.0647105451113958e-52 is_normal=False)\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"2026-06-12 23:33:43,332 - ml4t.diagnostic.evaluation.distribution.tails - INFO - Starting comprehensive tail analysis\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
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"2026-06-12 23:33:43,332 - ml4t.diagnostic.evaluation.distribution.tails - INFO - Starting comprehensive tail analysis\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"2026-06-12 23:33:43,332 - ml4t.diagnostic.evaluation.distribution.tails - INFO - Computing Hill estimator (n_obs=4780 k=69 tail=both)\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"2026-06-12 23:33:43,332 - ml4t.diagnostic.evaluation.distribution.tails - INFO - Computing Hill estimator (n_obs=4780 k=69 tail=both)\n"
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]
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"2026-06-12 23:33:43,333 - ml4t.diagnostic.evaluation.distribution.tails - INFO - Hill estimator computed (alpha=2.861623851699361 classification=medium k=69)\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"2026-06-12 23:33:43,333 - ml4t.diagnostic.evaluation.distribution.tails - INFO - Hill estimator computed (alpha=2.861623851699361 classification=medium k=69)\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"2026-06-12 23:33:43,333 - ml4t.diagnostic.evaluation.distribution.tails - INFO - Generating QQ plot data (n_obs=4780 distribution=normal)\n"
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]
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},
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"2026-06-12 23:33:43,333 - ml4t.diagnostic.evaluation.distribution.tails - INFO - Generating QQ plot data (n_obs=4780 distribution=normal)\n"
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]
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"2026-06-12 23:33:43,334 - ml4t.diagnostic.evaluation.distribution.tails - INFO - QQ plot data generated (distribution=normal r_squared=0.8696953632267342)\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"2026-06-12 23:33:43,334 - ml4t.diagnostic.evaluation.distribution.tails - INFO - QQ plot data generated (distribution=normal r_squared=0.8696953632267342)\n"
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]
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},
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"2026-06-12 23:33:43,334 - ml4t.diagnostic.evaluation.distribution.tails - INFO - Generating QQ plot data (n_obs=4780 distribution=t)\n"
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]
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},
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"2026-06-12 23:33:43,334 - ml4t.diagnostic.evaluation.distribution.tails - INFO - Generating QQ plot data (n_obs=4780 distribution=t)\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"2026-06-12 23:33:43,335 - ml4t.diagnostic.evaluation.distribution.tails - INFO - QQ plot data generated (distribution=t r_squared=0.9930260811121839)\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"2026-06-12 23:33:43,335 - ml4t.diagnostic.evaluation.distribution.tails - INFO - QQ plot data generated (distribution=t r_squared=0.9930260811121839)\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"2026-06-12 23:33:43,336 - ml4t.diagnostic.evaluation.distribution.tails - INFO - Tail analysis completed (tail_index=2.861623851699361 classification=medium best_fit=t)\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"2026-06-12 23:33:43,336 - ml4t.diagnostic.evaluation.distribution.tails - INFO - Tail analysis completed (tail_index=2.861623851699361 classification=medium best_fit=t)\n"
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]
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},
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{
|
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"name": "stderr",
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"output_type": "stream",
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"text": [
|
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"2026-06-12 23:33:43,336 - ml4t.diagnostic.evaluation.distribution - INFO - Distribution analysis completed (is_normal=False recommended=t n_obs=4780)\n"
|
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]
|
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},
|
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{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
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"text": [
|
|
"=== ml4t-diagnostic: Distribution Analysis ===\n",
|
|
"Mean: 0.0470\n",
|
|
"Std: 1.2210\n",
|
|
"Skewness: -0.0810\n",
|
|
"Excess kurtosis: 14.5642\n",
|
|
"Jarque-Bera p-value: 0.000000\n",
|
|
"Normal: False\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"dist_result = analyze_distribution(returns.dropna().values)\n",
|
|
"print(\"=== ml4t-diagnostic: Distribution Analysis ===\")\n",
|
|
"m = dist_result.moments_result\n",
|
|
"print(f\"Mean: {m.mean:.4f}\")\n",
|
|
"print(f\"Std: {m.std:.4f}\")\n",
|
|
"print(f\"Skewness: {m.skewness:.4f}\")\n",
|
|
"print(f\"Excess kurtosis: {m.excess_kurtosis:.4f}\")\n",
|
|
"print(f\"Jarque-Bera p-value: {dist_result.jarque_bera_result.p_value:.6f}\")\n",
|
|
"print(f\"Normal: {dist_result.is_normal}\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "9072a64f",
|
|
"metadata": {
|
|
"papermill": {
|
|
"duration": 0.005295,
|
|
"end_time": "2026-06-13T03:33:43.348972+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-13T03:33:43.343677+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"source": [
|
|
"**Interpretation**: Excess kurtosis >> 3 confirms fat tails (extreme returns\n",
|
|
"more frequent than normal); the Jarque-Bera test strongly rejects normality.\n",
|
|
"This motivates heavy-tailed distributions (Student-t) in GARCH models\n",
|
|
"and non-parametric approaches for VaR."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "3978aebc",
|
|
"metadata": {
|
|
"papermill": {
|
|
"duration": 0.005632,
|
|
"end_time": "2026-06-13T03:33:43.359909+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-13T03:33:43.354277+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"source": [
|
|
"## ARCH Effects: Bridge to GARCH (NB08)\n",
|
|
"\n",
|
|
"The Ljung-Box test on squared returns already suggested volatility clustering.\n",
|
|
"The formal ARCH-LM test confirms whether conditional heteroskedasticity is\n",
|
|
"present — the key prerequisite for GARCH modeling."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"id": "d298f65e",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2026-06-13T03:33:43.371798Z",
|
|
"iopub.status.busy": "2026-06-13T03:33:43.371689Z",
|
|
"iopub.status.idle": "2026-06-13T03:33:43.376151Z",
|
|
"shell.execute_reply": "2026-06-13T03:33:43.375851Z"
|
|
},
|
|
"papermill": {
|
|
"duration": 0.011078,
|
|
"end_time": "2026-06-13T03:33:43.376497+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-13T03:33:43.365419+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"2026-06-12 23:33:43,374 - ml4t.diagnostic.evaluation.volatility.arch - INFO - ARCH-LM test complete: statistic=1478.2388, p-value=0.0000 (lags=12 n_obs=4780)\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"=== ml4t-diagnostic: ARCH-LM Test ===\n",
|
|
"Test statistic: 1478.2388\n",
|
|
"P-value: 0.000000\n",
|
|
"ARCH effects: True\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"arch_result = arch_lm_test(returns.dropna().values)\n",
|
|
"print(\"=== ml4t-diagnostic: ARCH-LM Test ===\")\n",
|
|
"print(f\"Test statistic: {arch_result.test_statistic:.4f}\")\n",
|
|
"print(f\"P-value: {arch_result.p_value:.6f}\")\n",
|
|
"print(f\"ARCH effects: {arch_result.has_arch_effects}\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "e205ab76",
|
|
"metadata": {
|
|
"papermill": {
|
|
"duration": 0.005591,
|
|
"end_time": "2026-06-13T03:33:43.389037+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-13T03:33:43.383446+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"source": [
|
|
"**Bridge to NB08**: Strong ARCH effects confirm that constant-variance models\n",
|
|
"are inadequate. GARCH(1,1) is the natural next step — see `08_garch_volatility`\n",
|
|
"for fitting, diagnostics, and feature extraction."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "d7b5010f",
|
|
"metadata": {
|
|
"papermill": {
|
|
"duration": 0.005433,
|
|
"end_time": "2026-06-13T03:33:43.400167+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-13T03:33:43.394734+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"source": [
|
|
"## Feature Catalog: Diagnostic Features\n",
|
|
"\n",
|
|
"| Feature | Source | Computation | Update |\n",
|
|
"|---------|--------|-------------|--------|\n",
|
|
"| `adf_statistic` | Unit root test | Rolling 252-day ADF | Weekly |\n",
|
|
"| `adf_pvalue` | Unit root test | P-value from ADF | Weekly |\n",
|
|
"| `kpss_statistic` | Unit root test | Rolling 252-day KPSS | Weekly |\n",
|
|
"| `stationarity_regime` | Combined | Joint decision matrix | Weekly |"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "e854a8ea",
|
|
"metadata": {
|
|
"papermill": {
|
|
"duration": 0.005409,
|
|
"end_time": "2026-06-13T03:33:43.410971+00:00",
|
|
"exception": false,
|
|
"start_time": "2026-06-13T03:33:43.405562+00:00",
|
|
"status": "completed"
|
|
}
|
|
},
|
|
"source": [
|
|
"## Key Takeaways\n",
|
|
"\n",
|
|
"1. **Visual inspection first**: time series plot, ACF/PACF, Q-Q reveal\n",
|
|
" trends, dependence, and distributional properties at a glance\n",
|
|
"2. **Use both ADF and KPSS**: opposite null hypotheses give robust\n",
|
|
" conclusions via the decision matrix\n",
|
|
"3. **Returns are stationary, prices are not**: first-differencing is the\n",
|
|
" standard fix, but fractional differencing preserves memory (see NB03)\n",
|
|
"4. **Squared returns show ARCH effects**: volatility clusters, motivating\n",
|
|
" GARCH models (NB08)\n",
|
|
"5. **Rolling stationarity statistics** become time-varying features that\n",
|
|
" detect regime changes in real time\n",
|
|
"6. **ml4t-diagnostic consolidates diagnostics**: `analyze_stationarity()`\n",
|
|
" provides three-test consensus; `analyze_autocorrelation()` suggests ARIMA\n",
|
|
" orders; `analyze_distribution()` and `arch_lm_test()` complete the\n",
|
|
" pre-modeling diagnostic workflow\n",
|
|
"\n",
|
|
"**Next**: See `02_structural_breaks` for break detection and\n",
|
|
"`03_fractional_differencing` for memory-preserving stationarity transforms."
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.14.3"
|
|
},
|
|
"papermill": {
|
|
"default_parameters": {},
|
|
"duration": 8.246522,
|
|
"end_time": "2026-06-13T03:33:44.432485+00:00",
|
|
"environment_variables": {},
|
|
"exception": null,
|
|
"input_path": "09_model_based_features/01_visual_diagnostics.ipynb",
|
|
"output_path": "09_model_based_features/01_visual_diagnostics.ipynb",
|
|
"parameters": {},
|
|
"start_time": "2026-06-13T03:33:36.185963+00:00",
|
|
"version": "2.7.0"
|
|
},
|
|
"jupytext": {
|
|
"cell_metadata_filter": "tags,-all"
|
|
},
|
|
"ml4t_provenance": {
|
|
"source_py_blob": "95a1db6da99f886eed9c105c907f8c32bdee60ae",
|
|
"executed_at": "2026-06-13T03:33:44.644189+00:00",
|
|
"executor": "cpu-local",
|
|
"production": true,
|
|
"parameters": {},
|
|
"notes": "executed-state finalization batch (2026-06-13 run)"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|