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+# TimesFM
+
+TimesFM (Time Series Foundation Model) is a pretrained time-series foundation
+model developed by Google Research for time-series forecasting.
+
+*   Paper:
+    [A decoder-only foundation model for time-series forecasting](https://arxiv.org/abs/2310.10688),
+    ICML 2024.
+*   All checkpoints:
+    [TimesFM Hugging Face Collection](https://huggingface.co/collections/google/timesfm-release-66e4be5fdb56e960c1e482a6).
+*   [Google Research blog](https://research.google/blog/a-decoder-only-foundation-model-for-time-series-forecasting/).
+*   TimesFM in Google 1P Products:
+    *   [BigQuery ML](https://cloud.google.com/bigquery/docs/timesfm-model): Enterprise level SQL queries for scalability and reliability.
+    *   [Google Sheets](https://workspaceupdates.googleblog.com/2026/02/forecast-data-in-connected-sheets-BigQueryML-TimesFM.html): For your daily spreadsheet. 
+    *   [Vertex Model Garden](https://pantheon.corp.google.com/vertex-ai/publishers/google/model-garden/timesfm): Dockerized endpoint for agentic calling.
+
+This open version is not an officially supported Google product.
+
+**Latest Model Version:** TimesFM 2.5
+
+**Archived Model Versions:**
+
+-   1.0 and 2.0: relevant code archived in the sub directory `v1`. You can `pip
+    install timesfm==1.3.0` to install an older version of this package to load
+    them.
+## Update - July 2, 2026
+
+Updated PyPI to `timesfm=2.0.2`. See [Install](https://github.com/google-research/timesfm#from-pypi).
+
+## Update - Apr. 9, 2026
+
+Added fine-tuning example using HuggingFace Transformers + PEFT (LoRA) — see
+[`timesfm-forecasting/examples/finetuning/`](timesfm-forecasting/examples/finetuning/).
+Also added unit tests (`tests/`) and incorporated several community fixes.
+
+Shoutout to [@kashif](https://github.com/kashif) and [@darkpowerxo](https://github.com/darkpowerxo). 
+
+## Update - Mar. 19, 2026
+
+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.
+
+## Update - Oct. 29, 2025
+
+Added back the covariate support through XReg for TimesFM 2.5.
+
+
+## Update - Sept. 15, 2025
+
+TimesFM 2.5 is out!
+
+Comparing to TimesFM 2.0, this new 2.5 model:
+
+-   uses 200M parameters, down from 500M.
+-   supports up to 16k context length, up from 2048.
+-   supports continuous quantile forecast up to 1k horizon via an optional 30M
+    quantile head.
+-   gets rid of the `frequency` indicator.
+-   has a couple of new forecasting flags.
+
+Since the Sept. 2025 launch, the following improvements have been completed:
+
+1.  ✅ Flax version of the model for faster inference.
+2.  ✅ Covariate support via XReg (see Oct. 2025 update).
+3.  ✅ Documentation, examples, and agent skill (see `timesfm-forecasting/`).
+4.  ✅ Fine-tuning example with LoRA via HuggingFace Transformers + PEFT (see `timesfm-forecasting/examples/finetuning/`).
+5.  ✅ Unit tests for core layers, configs, and utilities (see `tests/`).
+
+### Install
+
+#### From `PyPI`
+
+```shell
+# Install the package with torch
+pip install timesfm[torch]
+# Or with Flax
+pip install timesfm[flax]
+# And when XReg is needed
+pip install timesfm[xreg]
+```
+
+#### Local Install
+
+1.  Clone the repository:
+    ```shell
+    git clone https://github.com/google-research/timesfm.git
+    cd timesfm
+    ```
+
+2.  Create a virtual environment and install dependencies using `uv`:
+    ```shell
+    # Create a virtual environment
+    uv venv
+    
+    # Activate the environment
+    source .venv/bin/activate
+    
+    # Install the package in editable mode with torch
+    uv pip install -e .[torch]
+    # Or with flax
+    uv pip install -e .[flax]
+    # And when XReg is needed
+    uv pip install -e .[xreg]
+    ```
+
+3. [Optional] Install your preferred `torch` / `jax` backend based on your OS and accelerators
+(CPU, GPU, TPU or Apple Silicon).:
+
+-   [Install PyTorch](https://pytorch.org/get-started/locally/).
+-   [Install Jax](https://docs.jax.dev/en/latest/installation.html#installation)
+    for Flax.
+
+### Code Example
+
+```python
+import torch
+import numpy as np
+import timesfm
+
+torch.set_float32_matmul_precision("high")
+
+model = timesfm.TimesFM_2p5_200M_torch.from_pretrained("google/timesfm-2.5-200m-pytorch")
+
+model.compile(
+    timesfm.ForecastConfig(
+        max_context=1024,
+        max_horizon=256,
+        normalize_inputs=True,
+        use_continuous_quantile_head=True,
+        force_flip_invariance=True,
+        infer_is_positive=True,
+        fix_quantile_crossing=True,
+    )
+)
+point_forecast, quantile_forecast = model.forecast(
+    horizon=12,
+    inputs=[
+        np.linspace(0, 1, 100),
+        np.sin(np.linspace(0, 20, 67)),
+    ],  # Two dummy inputs
+)
+point_forecast.shape  # (2, 12)
+quantile_forecast.shape  # (2, 12, 10): mean, then 10th to 90th quantiles.
+```