> 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/api/forecasts/overview.md).

# Overview

The Forecasts API delivers event-driven demand forecasts without you building or maintaining a forecasting model.

You supply historical demand data per location. The API trains a model, applies [Beam](/api/beam/overview.md) automatically for feature selection, and returns daily-level forecasts with the event impact behind each day available for explainability. A baseline comparison metric shows the accuracy improvement attributable to PredictHQ data, measured on your own demand.

The workflow maps to the API's resources:

1. Create a [model](/api/forecasts/models.md) for a Saved Location and choose an [algorithm](/api/forecasts/algorithms.md) (most integrations use the default).
2. Upload historical [demand data](/api/forecasts/demand-data.md).
3. Train the model.
4. Retrieve [forecasts](/api/forecasts/forecasts.md) on your forecast cadence.
5. Retrain the model as new demand data accumulates.

Use the Forecasts API when time-to-value matters more than owning the model. For full control over model architecture and feature engineering, use the [Features API](/api/features/get-features.md) with a `beam.analysis_id` instead.

The Forecasts API also fits multi-model setups: run it as one candidate in a champion-challenger selection or an ensemble alongside your existing forecasts, and let measured accuracy decide which wins each series. Nothing needs replacing to adopt it.

## Guides

The following guides cover the Forecasts API:

* [Getting started with Forecasts API](/getting-started/guides/forecasts-api-guides/getting-started.md)
* [Understanding forecast accuracy metrics](/getting-started/guides/forecasts-api-guides/understanding-forecast-accuracy-metrics.md)
* [Troubleshooting guide for Forecasts API](/getting-started/guides/forecasts-api-guides/troubleshooting.md)


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation by asking a question.

Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:

```
GET https://docs.predicthq.com/api/forecasts/overview.md?ask=<question>&goal=<user_goal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.