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name: feature-engineering description: "Generate StatsForecast synthetic fixtures, MSTL trend/seasonal features, future X_df regressors, and static/exogenous feature panels." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

StatsForecast feature engineering

Use this sub-skill when a task is about constructing or validating StatsForecast input features, especially synthetic panels, AirPassengers fixtures, MSTL trend/seasonal component features, future X_df rows, and static/exogenous column conventions for pandas or polars data.

Load these files

• Feature workflows: end-to-end recipes for generate_series, AirPassengers fixtures, mstl_decomposition, future X_df, static features, and pandas/polars validation.
• API reference: signatures, imports, dataframe schemas, output columns, and X_df shape contracts.
• Troubleshooting: fixes for invalid MSTL models, unsorted panels, horizon/frequency mismatches, optional polars availability, and future-exogenous column alignment.
• MSTL feature smoke script: a tiny runtime check that builds data, runs MSTL(season_length=7) decomposition, and asserts future X_df rows.

Fast routing

1. If the user needs to create a small example panel, use generate_series or AirPassengersDF from Feature workflows.
2. If the user wants trend/seasonal regressors for a downstream model, use mstl_decomposition and hand the returned train_df plus X_df to the forecasting workflow.
3. If the user has extra training columns, decide whether each one is a static feature or a time-varying exogenous regressor, then ensure future rows carry the same needed feature columns.
4. If the issue is final forecast execution, prediction intervals, fitted values, or persistence, route to core-forecasting after preparing features.
5. If the issue is choosing the downstream model family or checking whether a model uses exogenous variables, route to model-selection.
6. If the issue is Dask, Ray, Spark, Fugue, or local multiprocessing behavior, route to distributed-execution.

Operating cautions

• mstl_decomposition expects canonical columns unique_id, ds, and y; rename custom columns before decomposition.
• The function requires an instantiated statsforecast.models.MSTL, not a different StatsForecast model.
• Treat the returned X_df as the future exogenous table for MSTL-derived features. It must have exactly h * number_of_series rows before it is passed to a model that uses exogenous regressors.
• Runtime files here are self-contained; do not depend on source repository notebooks, tests, docs, or local checkout paths.