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Time-series modeling

Time-series modeling is a recommended practice for data science problems where conditions may change over time. With this method, the validation set is made up of observations from a time window outside of (and more recent than) the time window used for model training. Time-aware modeling can make predictions on a single row, or, with its core time series functionality, can extract patterns from recent history and forecast multiple events into the future.

Topic Description
What is time-based modeling? Learn about the basic modeling process and a recommended reading path.
Time series workflow overview View the workflow for creating a time series project.
Time series insights Explore visualizations available to help interpret your data and models.
Time series predictions Make predictions with time series models.
Multiseries modeling Model with datasets that contain multiple time series.
Creating clusters Allow DataRobot to identify natural segments (similar series) for further exploring your data.
Segmented modeling Group series into user-defined segments, creating multiple projects for each segment, and producing a single Combined Model for the data.
Nowcasting Make predictions for the present and very near future (very short-range forecasting).
Enable external prediction comparison Compare model predictions built outside of DataRobot against DataRobot predictions.
Advanced time series modeling Modify partitions, set advanced options, and understand window settings.
Time series modeling data Work with the time series modeling dataset:
  • Creating the modeling dataset
  • Using the data prep tool
  • Restoring pruned features
Time series reference Learn to customize time series projects and view a variety of deep-dive reference material for DataRobot time series modeling, as well as ime series considerations

Updated June 24, 2024