> For the complete documentation index, see [llms.txt](https://docs.predicthq.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.predicthq.com/getting-started/guides/features-api-guides.md).

# Features API guides

Guides for the Features API - model-ready demand signals engineered from real-world events. Always call it with a `beam.analysis_id`; run [Beam](/getting-started/guides/beam-guides.md) first if you haven't.

Recommended order:

1. [What is the Features API?](/getting-started/core-concepts/what-is-the-features-api.md) - why engineered features beat manual event aggregation
2. [Get features with the Features API](https://github.com/predicthq/phq-data-science-docs/blob/master/demand-forecasting-with-events/get-features-with-features-api.ipynb) - the retrieval notebook
3. [Improving demand forecasting models with event features](/getting-started/guides/features-api-guides/improving-demand-forecasting-models-with-event-features.md) - the worked walkthrough, from finding relevant events to a forecasting model with event features
4. [Demand forecasting with event features](https://github.com/predicthq/phq-data-science-docs/blob/master/demand-forecasting-with-events/demand-forecasting-with-event-features.ipynb) - the full modeling workflow notebook
5. [Forecasting demand in Power BI with event features](/getting-started/guides/features-api-guides/integrate-with-a-demand-forecast-in-powerbi.md) - the same workflow demonstrated end to end in Power BI AutoML
6. [Using event features with time series foundation models](/getting-started/guides/features-api-guides/using-event-features-with-time-series-foundation-models.md) - features as future covariates for pre-trained models

Remember the serving path: models trained on these features need future-dated features at every forecast run - see the [Features API reference](/api/features/get-features.md) for training and inference windows.


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