docs: make Chinese README the default
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<!-- WEHUB_ZH_README -->
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> [!NOTE]
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> 本文档由 WeHub 基于上游 README 翻译整理，属于社区翻译，非官方中文文档。
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> [English](./README.en.md) · [原始项目](https://github.com/google-research/timesfm) · [上游 README](https://github.com/google-research/timesfm/blob/HEAD/README.md)
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> 原作者、版权与许可证归属以原始项目及本仓库 LICENSE 文件为准。
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# TimesFM
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TimesFM (Time Series Foundation Model) is a pretrained time-series foundation
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model developed by Google Research for time-series forecasting.
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TimesFM（Time Series Foundation Model，时间序列基础模型）是 Google Research 开发的预训练时间序列基础模型，用于时间序列预测。
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* Paper:
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* 论文：
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[A decoder-only foundation model for time-series forecasting](https://arxiv.org/abs/2310.10688),
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ICML 2024.
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* All checkpoints:
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ICML 2024。
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* 全部检查点：
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[TimesFM Hugging Face Collection](https://huggingface.co/collections/google/timesfm-release-66e4be5fdb56e960c1e482a6).
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* [Google Research blog](https://research.google/blog/a-decoder-only-foundation-model-for-time-series-forecasting/).
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* TimesFM in Google 1P Products:
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* [BigQuery ML](https://cloud.google.com/bigquery/docs/timesfm-model): Enterprise level SQL queries for scalability and reliability.
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* [Google Sheets](https://workspaceupdates.googleblog.com/2026/02/forecast-data-in-connected-sheets-BigQueryML-TimesFM.html): For your daily spreadsheet.
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* [Vertex Model Garden](https://pantheon.corp.google.com/vertex-ai/publishers/google/model-garden/timesfm): Dockerized endpoint for agentic calling.
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* TimesFM 在 Google 第一方产品中的应用：
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* [BigQuery ML](https://cloud.google.com/bigquery/docs/timesfm-model): 企业级 SQL 查询，兼具可扩展性与可靠性。
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* [Google Sheets](https://workspaceupdates.googleblog.com/2026/02/forecast-data-in-connected-sheets-BigQueryML-TimesFM.html): 适用于日常电子表格场景。
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* [Vertex Model Garden](https://pantheon.corp.google.com/vertex-ai/publishers/google/model-garden/timesfm): 容器化端点，支持 agent 式调用。
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This open version is not an officially supported Google product.
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此开源版本并非 Google 官方支持的产品。
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**Latest Model Version:** TimesFM 2.5
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**最新模型版本：** TimesFM 2.5
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**Archived Model Versions:**
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**已归档模型版本：**
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- 1.0 and 2.0: relevant code archived in the sub directory `v1`. You can `pip
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install timesfm==1.3.0` to install an older version of this package to load
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them.
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## Update - July 2, 2026
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- 1.0 和 2.0：相关代码已归档至子目录 `v1`。你可以 `pip
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install timesfm==1.3.0` 安装该软件包的旧版本以加载
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它们。
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## 更新 - 2026 年 7 月 2 日
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Updated PyPI to `timesfm=2.0.2`. See [Install](https://github.com/google-research/timesfm#from-pypi).
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已将 PyPI 更新至 `timesfm=2.0.2`。参见 [Install](https://github.com/google-research/timesfm#from-pypi).
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## Update - Apr. 9, 2026
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## 更新 - 2026 年 4 月 9 日
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Added fine-tuning example using HuggingFace Transformers + PEFT (LoRA) — see
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[`timesfm-forecasting/examples/finetuning/`](timesfm-forecasting/examples/finetuning/).
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Also added unit tests (`tests/`) and incorporated several community fixes.
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新增使用 HuggingFace Transformers + PEFT（LoRA）的微调示例 — 参见
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[`timesfm-forecasting/examples/finetuning/`](timesfm-forecasting/examples/finetuning/)。
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同时新增单元测试（`tests/`），并纳入多项社区修复。
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Shoutout to [@kashif](https://github.com/kashif) and [@darkpowerxo](https://github.com/darkpowerxo).
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特别鸣谢 [@kashif](https://github.com/kashif) 与 [@darkpowerxo](https://github.com/darkpowerxo).
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## Update - Mar. 19, 2026
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## 更新 - 2026 年 3 月 19 日
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Huge shoutout to [@borealBytes](https://github.com/borealBytes) for adding the support for [AGENTS](https://github.com/google-research/timesfm/blob/master/AGENTS.md)! TimesFM [SKILL.md](https://github.com/google-research/timesfm/tree/master/timesfm-forecasting) is out.
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大力感谢 [@borealBytes](https://github.com/borealBytes) 添加了对 [AGENTS](https://github.com/google-research/timesfm/blob/master/AGENTS.md)! 的支持；TimesFM [SKILL.md](https://github.com/google-research/timesfm/tree/master/timesfm-forecasting) 已发布。
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## Update - Oct. 29, 2025
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## 更新 - 2025 年 10 月 29 日
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Added back the covariate support through XReg for TimesFM 2.5.
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通过 XReg 为 TimesFM 2.5 重新加入协变量（covariate）支持。
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## Update - Sept. 15, 2025
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## 更新 - 2025 年 9 月 15 日
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TimesFM 2.5 is out!
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TimesFM 2.5 正式发布！
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Comparing to TimesFM 2.0, this new 2.5 model:
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与 TimesFM 2.0 相比，全新的 2.5 模型：
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- uses 200M parameters, down from 500M.
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- supports up to 16k context length, up from 2048.
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- supports continuous quantile forecast up to 1k horizon via an optional 30M
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quantile head.
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- gets rid of the `frequency` indicator.
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- has a couple of new forecasting flags.
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- 参数量为 200M，较此前的 500M 有所减少。
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- 支持最长 16k 上下文长度，较此前的 2048 大幅提升。
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- 通过可选的 30M quantile head，支持最长 1k 预测步长的连续分位数（quantile）预测。
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- 移除了 `frequency` 指示器。
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- 新增若干预测相关标志位。
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Since the Sept. 2025 launch, the following improvements have been completed:
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自 2025 年 9 月发布以来，已完成以下改进：
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1. ✅ Flax version of the model for faster inference.
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2. ✅ Covariate support via XReg (see Oct. 2025 update).
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3. ✅ Documentation, examples, and agent skill (see `timesfm-forecasting/`).
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4. ✅ Fine-tuning example with LoRA via HuggingFace Transformers + PEFT (see `timesfm-forecasting/examples/finetuning/`).
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5. ✅ Unit tests for core layers, configs, and utilities (see `tests/`).
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1. ✅ 提供 Flax 版本模型，推理更快。
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2. ✅ 通过 XReg 支持协变量（参见 2025 年 10 月更新）。
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3. ✅ 文档、示例与 agent skill（参见 `timesfm-forecasting/`）。
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4. ✅ 基于 HuggingFace Transformers + PEFT 的 LoRA 微调示例（参见 `timesfm-forecasting/examples/finetuning/`）。
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5. ✅ 核心层、配置与工具函数的单元测试（参见 `tests/`）。
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### Install
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### 安装
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#### From `PyPI`
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#### 通过 `PyPI` 安装
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```shell
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# Install the package with torch
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@@ -78,15 +82,15 @@ pip install timesfm[flax]
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pip install timesfm[xreg]
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```
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#### Local Install
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#### 本地安装
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1. Clone the repository:
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1. 克隆仓库：
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```shell
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git clone https://github.com/google-research/timesfm.git
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cd timesfm
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```
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2. Create a virtual environment and install dependencies using `uv`:
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2. 使用 `uv` 创建虚拟环境并安装依赖：
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```shell
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# Create a virtual environment
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uv venv
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@@ -102,14 +106,13 @@ pip install timesfm[xreg]
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uv pip install -e .[xreg]
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```
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3. [Optional] Install your preferred `torch` / `jax` backend based on your OS and accelerators
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(CPU, GPU, TPU or Apple Silicon).:
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3. [可选] 根据你的操作系统与加速器（CPU、GPU、TPU 或 Apple Silicon）安装偏好的 `torch` / `jax` 后端：
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- [Install PyTorch](https://pytorch.org/get-started/locally/).
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- [Install Jax](https://docs.jax.dev/en/latest/installation.html#installation)
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for Flax.
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用于 Flax。
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### Code Example
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### 代码示例
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```python
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import torch
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