AI forecasting for time series
The Time Series Foundation Model, developed by Google Research, offers a pre-trained structure for time series forecasting. The model is designed to provide general predictive capabilities on different data sets.
Updates
- September 8, 2026: Stars 30,360 → 31,902, latest release v3.0.0 (August 28, 2026).
- September 3, 2026: Stars 28,335 → 30,360, latest release v3.0.0 (August 28, 2026).
- August 31, 2026: Stars 27,185 → 28,335, latest release v3.0.0 (August 28, 2026).
- August 2, 2026: Stars 22,167 → 27,185, latest release v2.0.2 (July 2, 2026).
What you get
- Fast prediction with pre-trained base model
- 16k context length support
- Adaptation to different data sets with flexible structure
Installation
pip install timesfm[torch]
# Or with Flax
pip install timesfm[flax]
# And when XReg is needed
pip install timesfm[xreg]git clone https://github.com/google-research/timesfm.git
cd timesfmIf you don't write code
I want to do time series forecasting using the TimesFM library. How can I configure the 200M parameter structure with version 2.5 of this basic model developed by Google? How should I determine max_context and max_horizon values, especially during the compile phase of the model, and in what format should I provide data input to the forecast function? Can you explain with an example code structure?
Related dictionary terms
Links
TreScout did not build this tool · we found it in GitHub trends and wrote it up. This page describes the repository as of 2026-06-18: The star count and our text belong to that day, the repository may have changed since. Check the repository link for the current state. This page was machine-translated from the Turkish original · the Turkish version prevails.