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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting.
| Date | Stars |
|---|---|
| 2026-07-31 | 27155 |
| 2026-08-01 | 27155 |
| 2026-08-02 | 27190 |
| 2026-08-06 | 27190 |
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# 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.
* All checkpoints:
[TimesFM Hugging Face Collection](https://huggingface.co/collections/google/timesfm-release-66e4be5fdb56e960c1e482a6).
* [Google Research blog](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://pantheon.corp.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 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 - July 2, 2026
Updated PyPI to `timesfm=2.0.2`. See [Install](https://github.com/google-research/timesfm#from-pypi).
## Update - Apr. 9, 2026
Added fine-tuning example using HuggingFace Transformers + PEFT (LoRA) — see
[`timesfm-forecasting/examples/finetuning/`](timesfm-forecasting/examples/finetuning/).
Also added unit tests (`tests/`) and incorporated several community fixes.
Shoutout to [@kashif](https://github.com/kashif) and [@darkpowerxo](https://github.com/darkpowerxo).
## Update - Mar. 19, 2026
Huge shoutout to [@borealBytes](https://github.com/borealBytes) for adding the support for [AGENTS](https://github.com/google-research/timesfm/blob/master/AGENTS.md)! TimesFM [SKILL.md](https://github.com/google-research/timesfm/tree/master/timesfm-forecasting) is out.
## Update - Oct. 29, 2025
Added back the covariate support through XReg for TimesFM 2.5.
## 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.
Since the Sept. 2025 launch, the following improvements have been completed:
1. ✅ Flax version of the model for faster inference.
2. ✅ Covariate support via XReg (see Oct. 2025 update).
3. ✅ Documentation, examples, and agent skill (see `timesfm-forecasting/`).
4. ✅ Fine-tuning example with LoRA via HuggingFace Transformers + PEFT (see `timesfm-forecasting/examples/finetuning/`).
5. ✅ Unit tests for core layers, configs, and utilities (see `tests/`).
### Install
#### From `PyPI`
```shell
# Install the package with torch
pip install timesfm[torch]
# Or with Flax
pip install timesfm[flax]
# And when XReg is needed
pip install timesfm[xreg]
```
#### Local Install
1. Clone the repository:
```shell
git clone https://github.com/google-research/timesfm.git
cd timesfm
```
2. Create a virtual environment and install dependencies using `uv`:
```shell
# Create a virtual environment
uv venv
# Activate the environment
source .venv/bin/activate
# Install the package in editable mode with torch
uv pip install -e .[torch]
# Or with flax
uv pip install -e .[flax]
# And when XReg is needed
uv pip install -e .[xreg]
```
3. [Optional] Install your preferred `torch` / `jax` backend based on your OS and accelerators
(CPU, GPU, TPU or Apple Silicon).:
- [Install PyTorch](https://pytorch.org/get-started/loExcerpt of 4,889 characters
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:a00f448bd5c2a6d4, llm:description: 'TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting.'
matched fp:a00f448bd5c2a6d4, llm:description: 'TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting.'
matched fp:a00f448bd5c2a6d4, llm:description: 'TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting.'