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name: transformations-pipelines description: "Transform time series, extract features, and compose sktime transformer and forecasting pipelines." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

Transformations and Pipelines

Use this sub-skill when the task is to transform sktime time-series data, extract features, repair missing values, create lag/window/difference/detrended series, or compose transformers with other estimators.

Route here

• Transform a univariate, multivariate, panel, or hierarchical time series.
• Extract features with SummaryTransformer, WindowSummarizer, Tabularizer, Rocket/MiniRocket, tsfresh, catch22, or related transformers.
• Difference, lag, impute, interpolate, detrend, deseasonalize, or create date, Fourier, and holiday features.
• Build transformer-only pipelines with *, +, TransformerPipeline, FeatureUnion, OptionalPassthrough, or make_pipeline.
• Decide whether a transformer belongs on target y via TransformedTargetForecaster or on exogenous X via ForecastingPipeline.

Route away

Forecasting objective design routes to forecasting; raw mtype/file conversion to data-interfaces; classifier/regressor/clusterer behavior to panel-learning.

References and helper

• API reference for signatures, categories, composition, pipeline semantics, and optional surfaces.
• Workflows for concrete fit/transform and pipeline recipes.
• Troubleshooting for mtype errors, index changes, inverse-transform failures, optional dependencies, and parameter names.
• Run scripts/transform_pipeline_smoke.py for an offline transformer and transformed-target pipeline check.