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name: preprocessing description: "Configure pmdarima target transforms, Fourier and date regressors, and ordered Pipeline integration with explicit inverse and exogenous-data contracts." disable-model-invocation: true metadata: disco-role: operating license: MIT

Preprocessing operating skill

Use this route when a pmdarima forecasting workflow needs a Box-Cox/log endogenous transform, Fourier seasonal regressors, calendar/date regressors, or pmdarima.pipeline.Pipeline composition. Read workflows for the decision and composition procedure, api-reference for exact signatures and shapes, and troubleshooting before relaxing a validation error or accepting a lossy inverse.

Fast routing

• Stabilize a positive or shifted target: choose BoxCoxEndogTransformer or LogEndogTransformer; decide the shift and non-positive policy first.
• Represent fixed seasonality as regressors: use FourierFeaturizer(m, k); it can synthesize future rows without future y.
• Encode known calendar effects: use DateFeaturizer(column_name=...) with a pandas datetime column; supply future dates explicitly.
• Chain stages: use named preprocessing transformers before a final pmdarima ARIMA/AutoARIMA in Pipeline.

Non-negotiable contracts

1. Endogenous transformers require y; exogenous transformers require X. Fourier is the exception at transform time because its fitted time index can generate features. DateFeaturizer always needs a DataFrame containing its configured datetime column.
2. fit_transform(y, X) returns (y_out, X_out), including when X was omitted. Pass both tuple items to the next stage; target transformers pass X through and feature transformers pass y through.
3. Fit only on training rows. Keep the seasonal period, harmonic count, date schema, shifts, and generated feature names fixed between fit and forecast.
4. In a pipeline, intermediate stages must be BaseTransformer instances and the last stage must be a pmdarima BaseARIMA estimator such as ARIMA or AutoARIMA. Pipeline preserves the supplied stage order and, at fit, clones each intermediate transformer (the final estimator is the supplied final step), records DataFrame feature order, and inverse-transforms endogenous predictions in reverse stage order by default.
5. A model fitted with exogenous columns needs compatible future X. Fourier can make its own future rows; DateFeaturizer cannot invent dates. Inspect pipe.transform(...) and its columns before calling predict(...).

Scope boundary

This skill covers transform configuration, tuple/inverse semantics, feature schema, and pipeline composition. It does not cover ARIMA order search, temporal cross-validation, or serialization/persistence/update lifecycle.

Bundled references

• API reference
• Workflows
• Troubleshooting

Evidence basis: pmdarima source tag v2.1.1, focused preprocessing and pipeline implementation/tests, and the repository's pipeline recipes. Runtime API smokes used the verified inspection environment; its reported package version was 0.0.0, so that environment fact is not treated as the source version.