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.
About this Accelerator
In this first installment of a three-part series on demand forecasting, this acceleratorprovides the building blocks for a time-series experimentation and production workflow.
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.
What you will learn
Inspect and handle common data and modeling challenges
Identifies common pitfalls in real-life time series data
Helper functions to scale experimentation with the tools mentioned above and more.