# Demand forecasting retraining
URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/df-retrain.html

> Implement retraining policies with DataRobot MLOps demand forecast deployments.

# Demand forecasting retraining

[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.

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