---
license: apache-2.0
library_name: timesfm
pipeline_tag: time-series-forecasting
---

# TimesFM

TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting.

**Resources and Technical Documentation**:
* Paper: [A decoder-only foundation model for time-series forecasting](https://arxiv.org/abs/2310.10688), ICML 2024.
* [Google Research blog](https://research.google/blog/a-decoder-only-foundation-model-for-time-series-forecasting/)
* [GitHub repo](https://github.com/google-research/timesfm)

**Authors**: Google Research

This checkpoint is not an officially supported Google product. See [TimesFM in BigQuery](https://cloud.google.com/bigquery/docs/timesfm-model) for Google official support.

## Checkpoint `timesfm-2.5-200m`

`timesfm-2.5-200m` is the third open model checkpoint.


### Data

`timesfm-2.5-200m` is pretrained using

- [GiftEvalPretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain)
- [Wikimedia Pageviews](https://meta.wikimedia.org/wiki/Pageviews_Analysis), cutoff Nov 2023 (see [paper](https://arxiv.org/abs/2310.10688) for details).
- [Google Trends](https://trends.google.com/trends/) top queries, cutoff EoY 2022 (see [paper](https://arxiv.org/abs/2310.10688) for details).
- Synthetic and augmented data.

### Install

`pip install` from PyPI coming soon. At this point, please run

```shell
git clone https://github.com/google-research/timesfm.git
cd timesfm
pip install -e .
```

### Code Example

```python
import numpy as np
import timesfm
model = timesfm.TimesFM_2p5_200M_torch()
model.load_checkpoint()
model.compile(
    timesfm.ForecastConfig(
        max_context=1024,
        max_horizon=256,
        normalize_inputs=True,
        use_continuous_quantile_head=True,
        force_flip_invariance=True,
        infer_is_positive=True,
        fix_quantile_crossing=True,
    )
)
point_forecast, quantile_forecast = model.forecast(
    horizon=12,
    inputs=[
        np.linspace(0, 1, 100),
        np.sin(np.linspace(0, 20, 67)),
    ],  # Two dummy inputs
)
point_forecast.shape  # (2, 12)
quantile_forecast.shape  # (2, 12, 10): mean, then 10th to 90th quantiles.
```