# Time series and specific use cases
URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/index.html

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

# Time series and specific use cases

| 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. |

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