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?||The basic modeling process and provides a recommended reading path.|
|Workflow overview||The workflow for creating a time series project.|
|Date/time partitioning||The underlying structure that supports time aware modeling.|
|Time series modeling||Building time series models and making predictions with them.|
|Multiseries modeling||Modeling with datasets that contain multiple time series.|
|Segmented modeling||Grouping series into segments, creating multiple projects for each segment, and producing a single combined model for the data.|
|Nowcasting||Making predictions for the present and very near future (very short-range forecasting).|
|Enable external prediction comparison||Comparing model predictions built outside of DataRobot against DataRobot predictions.|
|Time series modeling data||The time series modeling dataset and restoring pruned features.|
|Time series reference||Deep-dive reference material for DataRobot time series modeling.|