timesfm

# 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.
*   🆕 TimesFM 3.0 Checkpoint:
    [`google/timesfm-3.0-pytorch`](https://huggingface.co/google/timesfm-3.0-pytorch). Checkpoints up to 2.5:
    [TimesFM Hugging Face Collection](https://huggingface.co/collections/google/timesfm-release-66e4be5fdb56e960c1e482a6).
*   Google Reseach blog posts:
    * [TimesFM-3: A zero-shot foundation model for multivariate forecasting](https://research.google/blog/timesfm-3-a-zero-shot-foundation-model-for-multivariate-forecasting/)
    * [A decoder-only foundation model for time-series forecasting](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://console.cloud.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 3.0 ✨

**Archived Model Versions:**

-   2.5: relevant code under `src/timesfm`.
-   1.0 and 2.0: relevant code archived in the subdirectory `v1`. You can `pip
    install timesfm==1.3.0` to install an older version of this package to load
    them.

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## Update — September 2026

TimesFM 3.0 finishes rollout in Google Cloud [BigQuery ML](https://docs.cloud.google.com/bigquery/docs/timesfm-model), supporting commercial and production uses (see clarified [license notice](https://github.com/google-research/timesfm#license-notice-for-pretrained-weights)).

## Update — August 2026

**TimesFM 3.0 is out!**

TimesFM 3.0 introduces native **multivariate time-series forecasting**, flexible
**covariate support** (both past-only and past-and-future covariates), superior
zero-shot generalist capabilities, and top performance across all three major
time-series foundation model benchmarks.

### Key Highlights:

-   **Native Multivariate & Univariate Forecasting with Covariates**: Seamlessly
    forecast multi-channel multivariate series as well as individual univariate
    series, with native support for past-only and past-and-future dynamic
    covariates without per-task tuning.
-   **Top Benchmark Performance**:
    -   🥇 **fev-bench**: **Rank #1 overall** across 100 diverse real-world
        forecasting tasks.
    -   🥇 **TIME Benchmark**: **Rank #1 overall** across 50 domain data