Time-aware feature generation
None of the selected algorithms can use features generated here.
Specify how feature engineering should be applied to external features (see External Features).

For each external feature, you can define lag values and aggregates computed over one or more rolling windows.
These engineered features will be used by algorithms that support external features.
 will be applied to {{ listAlgosCompatibleWithShiftsWindows().length }} {{ "algorithm" | plurify:listAlgosCompatibleWithShiftsWindows().length }}. Which one?
Feature enabled in the External Features tab will appear here.

Time-shifted features Dataiku generates lagged features (or "negative shifts") by using data from the past.

Feature
Role
From forecast origin
From forecasted point
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Forecast origin shift
A feature value from a fixed time step before the forecast origin (when the forecast is made).
Example: A forecast origin shift of -2 uses the same value from 2 steps before the forecast origin for all the predicted values in the horizon.

Forecasted point shift (for multi-step forecasts)
A feature value from a time step that "slides" relative to each point in the forecast horizon.
Example: For a forecast from t+1 to t+2, a forecasted point shift of -7 uses the value at t-6 for the t+1 forecast and t-5 for the t+2 forecast.

Aggregate features Compute statistics like average, std dev, min, max, or most frequent over a time window (e.g., 35 to 0 days) before forecast origin or forecasted point.
Aggregates before the forecast origin stay the same for all forecast steps; those before each forecasted point update individually for every step in the forecast horizon.

Rolling window:
to {{ prettyTimeUnit(mlTaskDesign.timestepParams.timeunit) | plurify: (window.shift > 0 ? window.shift : -window.shift) }} from
Feature
Role
Aggregation methods
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{{ windowsValidationErrors[windowIndex].operations }}
{{ windowsValidationErrors[windowIndex].shift }}