# Time series and specific use cases

> Time series and specific use cases - Accelerators for time series and other specific use cases that
> you can add to your experiment workflow.

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 `2026-05-06T18:17:09.585255+00:00` (UTC).

## Primary page

- [Time series and specific use cases](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/index.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.
- [AML alert scoring](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/alert-scoring.html): Linked from this page.
- [Cold start demand forecasting](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/cold-start.html): Linked from this page.
- [Demand forecasting with Databricks](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/dbx-forecast.html): Linked from this page.
- [Time series demand forecasting](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/demand-flow.html): Linked from this page.
- [Demand forecasting retraining](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/df-retrain.html): Linked from this page.
- [Financial planning analysis](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/fin-plan.html): Linked from this page.
- [Flight delay prediction](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/flight-delays.html): Linked from this page.
- [Fraud detection with Neo4j](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/fraud-detection.html): Linked from this page.
- [Multi-model analysis](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/ml-analysis.html): Linked from this page.
- [Netlift modeling](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/ml-uplift.html): Linked from this page.
- [What-if demand forecasting](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/ml-what-if.html): Linked from this page.
- [No-show appointment prediction](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/no-show.html): Linked from this page.
- [Lumber price forecasting with Ready Signal](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/ready-signal.html): Linked from this page.
- [Recommendation engine](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/rec-engine.html): Linked from this page.
- [Panel data self-joins](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/self-joins.html): Linked from this page.
- [Technical price prediction](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/tech-prices.html): Linked from this page.
- [Statistical tests with Airflow](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/test-airflow.html): Linked from this page.
- [Trading volume profile curve](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/ts-factory.html): Linked from this page.
- [Hierarchical reconciliation](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/ts-recon.html): Linked from this page.
- [Visual AI for geospatial data](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/viz-geo.html): Linked from this page.

## Documentation content

| Topic | Description |
| --- | --- |
| AML alert scoring | Develop a machine learning model that utilizes historical data, including customer and transactional information, to identify alerts that resulted in the generation of a Suspicious Activity Report (SAR). |
| Cold start demand forecasting | This accelerator provides a framework to compare several approaches for cold start modeling on series with limited or no history. |
| Demand forecasting with Databricks | How to use DataRobot with Databricks to develop, evaluate, and deploy a multi-series demand forecasting model. |
| Time series demand forecasting | Perform large-scale demand forecasting using DataRobot's Python package. |
| Demand forecasting retraining | Implement retraining policies with DataRobot MLOps demand forecast deployments. |
| Financial planning analysis | This accelerator illustrates an end-to-end financial planning and analysis workflow in DataRobot. |
| Flight delay prediction | Designed for DataRobot trial users, experience an end-to-end DataRobot workflow using a use case that predicts flight delays. |
| Fraud detection with Neo4j | Build a fraud detection pipeline using Neo4j for storing and querying a knowledge graph. |
| Multi-model analysis | Use Python functions to aggregate DataRobot model insights into visualizations. |
| Netlift modeling | Leverage machine learning to find patterns around the types of people for whom marketing campaigns are most effective. |
| 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. |
| No-show appointment prediction | Build a model that identifies patients most likely to miss appointments, with correlating reasons. |
| Lumber price forecasting with Ready Signal | Use Ready Signal to add external control data, such as census and weather data, to improve time series predictions. |
| Recommendation engine | Explore how to use historical user purchase data in order to create a recommendation model, which will attempt to guess which products out of a basket of items the customer will be likely to purchase at a given point in time. |
| Panel data self-joins | Explore how to implement self-joins in panel data analysis. |
| Technical price prediction | Leverage historical insurance claim data for modeling and analysis. |
| Statistical tests with Airflow | Review an example workflow for carrying out statistical tests, notify stakeholders of any issues via Slack, and generate automated compliance documentation with the test results. |
| Trading volume profile curve | Use a framework to build models that will allow you to predict how much of the next day trading volume will happen at each time interval. |
| Hierarchical reconciliation | Learn how to reconcile independent time series forecasts with a hierarchical structure. |
| Visual AI for geospatial data | Learn how to use Visual AI to represent geospatial data for enhanced analysis. |