Demand forecasting models have many common challenges: large quantities of SKUs or series to predict, partial history or irregular history for many SKUs, multiple locations with different local or regional demand patterns, and cold-start prediction requests from the business for new products. The list goes on.
Time Series in DataRobot, however, has a diverse range of functionality to help tackle these challenges. For example:
- Automatic feature engineering and creation of lagged variables across multiple data types, as well as training dataset creation
- Diverse approaches for time series modeling with text data, learning from cross-series interactions and scaling to hundreds or thousands of series
- Feature generation from an uploaded Calendar of Events file specific to your business or use case.
- Automatic backtesting controls for regular and irregular time-series.
- Training dataset creation for irregular series via custom aggregations.
- Segmented modeling, hierarchical clustering for multi-series models, multimodal modeling, and ensembling.
- Periodicity and stationarity detection, and automatic feature list creation with various differencing strategies.
- Cold start modeling on series with limited or no history
- And insights for all of the above.
In this first installment of a three-part series on demand forecasting, this accelerator provides the building blocks for a time-series experimentation and production workflow. This notebook provides a framework to inspect and handle common data and modeling challenges, identifies common pitfalls in real-life time series data, and provides helper functions to scale experimentation with the tools mentioned above and more.
The dataset consists of 50 series (46 SKUs across 22 stores) over a 2 year period with varying series history, typical of a business releasing and removing products over time.