# Demand forecasting with Databricks

> Demand forecasting with Databricks - How to use DataRobot with Databricks to develop, evaluate, and
> deploy a multi-series demand forecasting model.

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 `2026-05-06T18:17:09.584534+00:00` (UTC).

## Primary page

- [Demand forecasting with Databricks](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/dbx-forecast.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/ecosystem_integration_templates/Databricks%20%26%20Datarobot%20-%20Large%20Scale%20Forecasting/Databricks%20%26%20Datarobot%20-%20Large%20Scale%20Forecasting.ipynb)

This accelerator is developed for use with Databricks to help you leverage the power of DataRobot for time-series modeling within a Databricks ecosystem.

Demand forecasting models are valuable to many businesses because they apply to high-value use cases such as improving inventory management, supply chain processes, and store staffing. However, building forecasting models can be challenging and time-consuming given the amount of experimentation typically required, from performing time series feature engineering to implementing diverse and complex time-series algorithms and evaluating results. The time series capabilities of DataRobot accelerate this process so you can rapidly build and test many modeling approaches and productionalize your models with model monitoring.

This accelerator can be imported into Databricks notebooks to walk you through how to use DataRobot with Databricks to develop, evaluate, and deploy a multi-series demand forecasting model. The notebook utilizes the DataRobot API to access DataRobot capabilities while ingesting data from Databricks for model building and scoring.

In this accelerator you will:

- Connect to DataRobot in a Databricks Notebook
- Import data from Databricks into the AI Catalog
- Create a time series forecasting project and run Autopilot
- Retrieve and evaluate model performances and insights
- Make new predictions with a test dataset
- Deploy a model with monitoring in DataRobot MLOps
- Forecast predictions via the Prediction API