Financial Planning and Analysis Workflow

[#1]

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