---

name: metrics-and-classifiers description: "Routes pyts DTW, lower-bound, and time-series classifier workflows for univariate series." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

metrics-and-classifiers

Use this sub-skill when the task is to compare two series with DTW/BOSS, choose a time-series metric, or fit a pyts classifier on univariate data.

What this covers

• Metrics and regions: boss, dtw, sakoe_chiba_band, itakura_parallelogram, show_options, and the DTW lower bounds.
• Classifiers: KNeighborsClassifier, BOSSVS, SAXVSM, TimeSeriesForest, TSBF, LearningShapelets.
• Metric-aware classification recipes such as metric='dtw' on GunPoint.
• Practical compatibility and performance notes for Numba and scikit-learn.

What this excludes

• Dataset loading: use ../datasets-and-loaders/SKILL.md.
• Preprocessing and symbolic building blocks: use ../preprocessing-and-symbols/SKILL.md.
• Feature extraction, images, and SSA: use ../feature-extraction-and-images/SKILL.md.
• Multivariate wrappers: use ../multivariate-workflows/SKILL.md.

Start here

1. Read references/workflows.md for the best-fit metric and classifier recipes.
2. Read references/api-reference.md for the verified signatures and option names.
3. Read references/troubleshooting.md when a DTW or classifier call fails on version skew, region shapes, or training cost.
4. Run scripts/smoke.py to confirm the installed package with a tiny metric/classifier check.

Useful triggers

• "DTW between two time series"
• "which Sakoe-Chiba band should I use?"
• "fit KNeighborsClassifier with dtw"
• "SAXVSM or BOSSVS on GunPoint"
• "why does dtw suddenly break after upgrading scikit-learn?"

Routing hints

• If the user only needs a pairwise distance, stay on the metric half of this sub-skill.
• If the user wants a classifier, route to the classifier half only after the metric or representation choice is clear.
• If the task is really about preprocessing or symbolic building blocks for a classifier, route there first and then return here for the modeling step.

Links

• Read references/workflows.md for the default DTW and classifier recipes.
• Read references/api-reference.md for the verified metric and classifier signatures.
• Read references/troubleshooting.md for the scikit-learn compatibility and DTW shape pitfalls.
• Run scripts/smoke.py for a quick installed-package check.