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name: preprocessing-and-symbols description: "Routes pyts sample-wise preprocessing, approximation, symbolic discretization, and bag-of-words workflows." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

preprocessing-and-symbols

Use this sub-skill when a user needs to scale, impute, discretize, approximate, or symbolically encode univariate time series.

What this covers

• Sample-wise preprocessing: StandardScaler, MinMaxScaler, MaxAbsScaler, RobustScaler, PowerTransformer, QuantileTransformer, KBinsDiscretizer, InterpolationImputer.
• Approximation and symbolic transforms: PiecewiseAggregateApproximation, SymbolicAggregateApproximation, DiscreteFourierTransform, MultipleCoefficientBinning, SymbolicFourierApproximation.
• Bag-of-words helpers: WordExtractor, BagOfWords.
• Shape, dtype, binning, and window-size constraints that make later modeling workflows predictable.

What this excludes

• Dataset loading: use ../datasets-and-loaders/SKILL.md.
• Feature extraction, images, and decomposition: use ../feature-extraction-and-images/SKILL.md.
• Metrics and classifiers: use ../metrics-and-classifiers/SKILL.md.
• Multivariate wrappers: use ../multivariate-workflows/SKILL.md.

Start here

1. Read references/workflows.md for the safe end-to-end recipes.
2. Read references/api-reference.md for the verified signatures and key parameter defaults.
3. Read references/troubleshooting.md when a preprocessing or symbolic call fails on NaNs, shapes, or invalid bin/window settings.
4. Run scripts/smoke.py to confirm the installed package and the symbolic workflow on tiny arrays.

Useful triggers

• "scale each time series independently"
• "impute missing values in a time series"
• "PAA/SAX/DFT/Bag-of-Words"
• "how do I discretize time series before classification?"
• "why does BagOfWords reject my input shape?"

Routing hints

• If the user wants a reusable symbolic feature representation, stay here long enough to choose the right transform and then route to the feature or classifier sub-skill if modeling is the real end goal.
• If the user is asking about WordExtractor or BagOfWords, remember that list-like symbolic input works better than an object array wrapper.
• If the user needs a downstream classifier or image transform, do not bury that logic here; hand off to the owning sub-skill.

Links

• Read references/workflows.md for the most common recipes.
• Read references/api-reference.md for the verified public signatures.
• Read references/troubleshooting.md for NaN, constant-array, and binning errors.
• Run scripts/smoke.py for a quick installed-package smoke test.