This notebook illustrates an end-to-end FP&A workflow in DataRobot. Time series forecasting in DataRobot has a huge suite of tools and approaches to handle highly complex multiseries problems.


DataRobot will be used for the model training, selection, deployment, and creating forecasts. While this example will leverage a snapshot file as a datasource this workflow applies to any data source, e.g. Redshift, S3, Big Query, Synapse, etc.


This notebook will demonstrate how to use the Python API client to:

  1. Connect to DataRobot
  2. Import and preparation of data for time series modeling
  3. Create a time series forecasting project and run Autopilot
  4. Retrieve and evaluate model performance and insights
  5. Making forward looking forecasts
  6. Evaluating forecasts vs. historical trends
  7. Deploy a model
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Last update:
‎01-15-2024 11:57 PM
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