Update README.md

README.md

@@ -1,9 +1,71 @@
 ---
-tags:
-- model_hub_mixin
+license: apache-2.0
+library_name: timesfm
+pipeline_tag: time-series-forecasting
 ---
 
-This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
-- Code: [More Information Needed]
-- Paper: [More Information Needed]
-- Docs: [More Information Needed]
+# 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.from_pretrained("google/timesfm-2.5-200m-pytorch", torch_compile=True)
+
+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.
+```