# Demand forecasting retraining

> Demand forecasting retraining - Implement retraining policies with DataRobot MLOps demand forecast
> deployments.

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 `2026-05-06T18:17:09.584769+00:00` (UTC).

## Primary page

- [Demand forecasting retraining](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/df-retrain.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.
- [first accelerator](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/demand-flow.html): Linked from this page.
- [second accelerator](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/cold-start.html): Linked from this page.
- [third accelerator](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/ml-what-if.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/Demand_forecasting3_retraining/End_to_end_demand_forecasting_retraining.ipynb)

This accelerator  demonstrates retraining policies with DataRobot MLOps demand forecast deployments.

This accelerator is a another installment of a series on demand forecasting. The [first accelerator](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/demand-flow.html) focuses on handling common data and modeling challenges, identifies common pitfalls in real-life time series data, and provides helper functions to scale experimentation. The [second accelerator](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/cold-start.html) provides the building blocks for cold start modeling workflow on series with limited or no history. They can be used as a starting point to create a model deployment for the app. The [third accelerator](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/ml-what-if.html) is a what-if app that allows you to adjust certain known in advance variable values to see how changes in those factors might affect the forecasted demand.