# What-if demand forecasting

> What-if demand forecasting - Discover how to use a what-if app to adjust known-in-advance variables
> and explore how changes in factors like promotions, pricing, or seasonality can impact demand
> forecasts.

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 `2026-05-06T18:17:09.585697+00:00` (UTC).

## Primary page

- [What-if demand forecasting](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/ml-what-if.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/tree/main/use_cases_and_horizontal_approaches/Demand_forecasting4_what_if_app/README.md)

This demand forecasting what-if app allows you to adjust certain known in advance variable values to see how changes in those factors might affect the forecasted demand.

Some examples of factors that might be adjusted include marketing promotions, pricing, seasonality, or competitor activity. By using the app to explore different scenarios and adjust key inputs, you can make more accurate predictions about future demand and plan accordingly.

This app is a third installment of a three-part series on demand forecasting. The [first accelerator](https://github.com/datarobot-community/ai-accelerators/tree/main/use_cases_and_horizontal_approaches/Demand_forecasting1_end_to_end/End_to_end_demand_forecasting.ipynb) 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://github.com/datarobot-community/ai-accelerators/tree/main/use_cases_and_horizontal_approaches/Demand_forecasting2_cold_start/End_to_end_demand_forecasting_cold_start.ipynb) 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.