timecopilot-timesfm 0.2.0

Open weights time-series foundation model from Google Research.

Disclaimer

This is a personal fork of timesfm, maintained to support custom changes for timecopilot and publish pypi wheels at timecopilot-timesfm. It may diverge from upstream.

Credits

This project is a fork of by Original Author(s).
All credit for the original code belongs to them. This fork is maintained independently to support TimeCopilot-specific changes.

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, ICML 2024.
• All checkpoints: TimesFM Hugging Face Collection.
• Google Research blog.
• TimesFM in BigQuery: an official Google product.

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 - 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.

Along with the model upgrade we have also upgraded the inference API. This repo will be under construction over the next few weeks to

1. add support for an upcoming Flax version of the model (faster inference).
2. add back covariate support.
3. populate more docstrings, docs and notebook.

Install

TODO(siriuz42): Package timesfm==2.0.0 and upload to PyPI .

Run

git clone https://github.com/google-research/timesfm.git
cd timesfm
pip install -e .

Code Example

import numpy as np
import timesfm
model = timesfm.TimesFM_2p5_200M_torch()
model.load_checkpoint()
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.