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--- |
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license: apache-2.0 |
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library_name: timesfm |
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pipeline_tag: time-series-forecasting |
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--- |
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# TimesFM |
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TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting. |
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**Resources and Technical Documentation**: |
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* Paper: [A decoder-only foundation model for time-series forecasting](https://arxiv.org/abs/2310.10688), ICML 2024. |
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* [Google Research blog](https://research.google/blog/a-decoder-only-foundation-model-for-time-series-forecasting/) |
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* [GitHub repo](https://github.com/google-research/timesfm) |
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**Authors**: Google Research |
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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. |
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## Checkpoint `timesfm-2.5-200m` |
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`timesfm-2.5-200m` is the third open model checkpoint. |
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### Data |
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`timesfm-2.5-200m` is pretrained using |
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- [GiftEvalPretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain) |
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- [Wikimedia Pageviews](https://meta.wikimedia.org/wiki/Pageviews_Analysis), cutoff Nov 2023 (see [paper](https://arxiv.org/abs/2310.10688) for details). |
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- [Google Trends](https://trends.google.com/trends/) top queries, cutoff EoY 2022 (see [paper](https://arxiv.org/abs/2310.10688) for details). |
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- Synthetic and augmented data. |
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### Install |
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`pip install` from PyPI coming soon. At this point, please run |
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```shell |
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git clone https://github.com/google-research/timesfm.git |
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cd timesfm |
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pip install -e . |
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``` |
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### Code Example |
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```python |
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import numpy as np |
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import timesfm |
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model = timesfm.TimesFM_2p5_200M_torch() |
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model.load_checkpoint() |
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model.compile( |
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timesfm.ForecastConfig( |
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max_context=1024, |
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max_horizon=256, |
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normalize_inputs=True, |
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use_continuous_quantile_head=True, |
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force_flip_invariance=True, |
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infer_is_positive=True, |
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fix_quantile_crossing=True, |
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) |
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) |
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point_forecast, quantile_forecast = model.forecast( |
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horizon=12, |
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inputs=[ |
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np.linspace(0, 1, 100), |
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np.sin(np.linspace(0, 20, 67)), |
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], # Two dummy inputs |
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) |
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point_forecast.shape # (2, 12) |
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quantile_forecast.shape # (2, 12, 10): mean, then 10th to 90th quantiles. |
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``` |
