Time series forecasting in DataRobot has a huge suite of tools and approaches to handle highly complex multiseries problems. These include:
Automatic feature engineering and the 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 an irregular series via custom aggregations.
Segmented modeling, hierarchical clustering for multi-series models, text support, 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.
Insights for models.
Data and accuracy drift monitoring.
About this Accelerator
This accelerator demonstrates retraining policies with DataRobot MLOps demand forecast deployments.
The dataset consists of 50 series (46 SKUs across 22 stores) over a two year period with varying series history, typical of a business releasing and removing products over time.
DataRobot will be used for the model training, selection, deployment, and making predictions. Snowflake will work as a data source for both training and testing, and as a storage to write predictions back. This workflow, however, applies to any data source, e.g. Redshift, S3, Big Query, Synapse, etc.
What you will learn
Ingest the data from Snowflake into AI Catalog within DataRobot
Run a new DataRobot project
Deploy the recommended model
Set up automated retraining
Define and run a job to make predictions and write them back into Snowflake
This accelerator is a another instalment of a series on demand forecasting.
The first accelerator focuses on handling common data and modeling challenges, identifies common pitfalls in real-life time series data, and provides helper functions to scale experimentation.
The second accelerator provides the building blocks for cold start modeling workflow on series with limited or no history. They can be used as a starting point to create a model deployment for the app.
The third accelerator is a what-if app that allows users to adjust certain known in advance variable values to see how changes in those factors might affect the forecasted demand.