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name: detection-distances description: "Use sktime detection, distance, kernel, and alignment APIs for anomaly, changepoint, segmentation, and pairwise similarity workflows." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

Detection, Distances, Kernels, and Alignment

Use this sub-skill for anomaly/outlier detection, changepoints, segmentation, detector outputs, pairwise distances/kernels, DTW-style choices, and alignment.

Route here

• Detect point anomalies, segment anomalies, changepoints, or segmentation boundaries.
• Use detector baselines such as ThresholdDetector or zero/dummy detectors.
• Construct pairwise distance or kernel matrices with ScipyDist, DTW wrappers, AggrDist, FlatDist, KernelFromDist, or alignment-derived distances.
• Diagnose sparse detector output, interval semantics, distance matrix shape, optional DTW dependencies, or kernel-vs-distance confusion.

Route away

Detection metrics route to evaluation-benchmarking; raw container validation to data-interfaces; panel classification/regression/clustering to panel-learning.

References and helper

• API reference for detector and pairwise transformer signatures.
• Workflows for detector and distance recipes.
• Troubleshooting for output interpretation and optional backend fallbacks.
• Run scripts/detection_distance_smoke.py for a tiny CPU check.