# Financial planning analysis

> Financial planning analysis - This accelerator illustrates an end-to-end financial planning and
> analysis workflow in DataRobot.

This Markdown file sits beside the HTML page at the same path (with a `.md` suffix). It summarizes the topic and lists links for tools and LLM context.

Companion generated at `2026-05-06T18:17:09.584896+00:00` (UTC).

## Primary page

- [Financial planning analysis](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/fin-plan.html): Full documentation for this topic (HTML).

## Related documentation

- [Developer documentation](https://docs.datarobot.com/en/docs/api/index.html): Linked from this page.
- [Developer learning](https://docs.datarobot.com/en/docs/api/dev-learning/index.html): Linked from this page.
- [AI accelerators](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/index.html): Linked from this page.
- [Time series and specific use cases](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/index.html): Linked from this page.

## Documentation content

[Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/use_cases_and_horizontal_approaches/FP%26A/FP%26A.ipynb)

This accelerator illustrates an end-to-end financial planning and analysis workflow in DataRobot. Time series forecasting in DataRobot has a huge suite of tools and approaches to handle highly complex multiseries problems. DataRobot is used for the model training, selection, deployment, and creation of forecasts. While this example will leverage a snapshot file as a data source, this workflow applies to any data source, e.g. Redshift, S3, Big Query, Synapse, etc.

This notebook will demonstrate how to use the Python API client to:

- Connect to DataRobot
- Import and preparation of data for time series modeling
- Create a time series forecasting project and run Autopilot
- Retrieve and evaluate model performance and insights
- Making forward looking forecasts
- Evaluating forecasts vs. historical trends
- Deploy a model