Time series modeling is not currently available for DataRobot Self Service users.
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.
|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:
|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 December 1, 2022
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