End-to-end demand forecasting and retraining workflow

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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.
  • Automated retraining.


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


Additional Resources

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.
Version history
Last update:
‎09-05-2023 10:06 PM
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