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name: supervised-models description: "Supervised time-series workflows for k-NN, GAK SVM/SVR, early classification, shapelets, and time-series MLPs." disable-model-invocation: true metadata: disco-role: operating license: BSD 2-Clause

Supervised Models

Use this sub-skill for tslearn supervised workflows built on tslearn.neighbors, tslearn.svm, tslearn.early_classification, tslearn.shapelets, and tslearn.neural_network. It covers k-NN search/classification/regression, GAK SVM/SVR, early classification, shapelets, and time-series MLPs.

Start here

• API reference
• Workflows
• Optional dependencies and backend setup
• Troubleshooting
• Smoke helper
• Root tslearn router for clustering, forecasting, serialization, matrix profile, or anything outside supervised time-series modeling

Route map

User task	Read
Nearest-neighbor search, classification, or regression; variable-length k-NN workflows	API reference, Workflows, Smoke helper
Time-series SVM/SVR with GAK, sklearn Pipeline/GridSearchCV, or probability caveats	API reference, Workflows
Early classification, streaming prediction, or earliness-vs-accuracy tuning	API reference, Workflows
Shapelets and backend/import-order setup	Optional dependencies and backend setup, Troubleshooting, Smoke helper
TimeSeriesMLP classifier/regressor workflows	API reference, Workflows

Operating rules

1. Keep sklearn interoperability explicit. These estimators are designed to fit into standard fit/predict, Pipeline, GridSearchCV, and cross_validate flows.
2. Use variable-length-aware supervised paths first: k-NN with time-series metrics, TimeSeriesSVC/TimeSeriesSVR with kernel="gak", or LearningShapelets when the shapelet-length and max_size constraints are satisfied.
3. TimeSeriesMLPClassifier and TimeSeriesMLPRegressor require equal-sized time series and flatten the input before handing it to scikit-learn's MLP implementation.
4. For LearningShapelets, set KERAS_BACKEND before the first keras import. If keras was imported too early, restart the process and re-run the import from a clean environment.
5. Do not use this sub-skill for clustering, forecasting, serialization, or matrix-profile tasks. Send those back to the root router instead.
6. Treat the bundled smoke helper as a tiny fit/predict check, not a benchmark and not a substitute for full example notebooks.