google-research/timesfm
TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting.
About the project
TimesFM is a pretrained time-series foundation model from Google Research for forecasting, designed to work zero-shot without task-specific training.
Useful for
- Forecast sales or metrics from historical series without training a custom model
- Get quantile forecast intervals to quantify prediction uncertainty
- Fine-tune the model on your own data via LoRA and HuggingFace PEFT
README summarized by DeepSeek V4.1 Flash. Details may be inaccurate.
Why it’s trending
- 75 stars so far today, about 92 expected by the end of the day.
- GitHub Trending this month: #14, +5,145 stars.
- About 14 forks a day — people are taking the code.
Stars per day
Bars are daily stars, the line is the usual pace. Red marks spike days.
Numbers
- Total stars
- 33,121
- Stars in a day
- 92
- Forks
- 3,187
- Issues and pull requests
- 485
- Watchers
- 220
- Language
- Python
- License
- Apache-2.0
- Latest release
- v3.0.0 · August 28, 2026
- Created
- April 29, 2024
- Last push
- September 15, 2026
Star trust
Growth looks organic: forks and discussion are in line with active projects, and stars arrive unevenly, the way people give them.
These are heuristics, not a verdict: we judge by the repository’s behavior, not by a list of stargazers.
Spotted in
- September 18, 2026GitHub Trending Python today: #7, +248 stars; GitHub Trending this month: #14, +5,145 stars
- September 17, 2026GitHub Trending Python today: #7, +272 stars; GitHub Trending this month: #15, +5,249 stars
- September 16, 2026GitHub Trending this month: #17, +5,202 stars
- September 15, 2026GitHub Trending this month: #16, +5,252 stars
- September 14, 2026GitHub Trending this month: #19, +5,192 stars
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