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 27, 2026: Stars 31,902 → 33,792, latest release v3.0.0 (August 28, 2026).
- 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).
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?
Repository: https://github.com/google-research/timesfm. Follow the official instructions in the README of this repository for setup and execution.
Before starting, check which OS we are on and if the necessary tools (e.g., Python, Node.js, Git, Docker) are installed. Install any missing ones step-by-step according to this system.
Create a new, empty folder to work in. Do not touch any files outside this folder and do not delete anything. Tell me what you are going to do in one sentence before running each command.
Tell me what I should see on the screen at the end of each step. If an error occurs, I will paste the error message to you exactly as it is, and we will fix it together.
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. The introductory text was prepared with AI from the repository's own documentation, and this page was machine-translated from the Turkish original · the Turkish version prevails.