---

name: tslearn description: "Routes tslearn users to data preparation, metrics/backends, clustering, supervised models, forecasting, and analysis/persistence workflows." disable-model-invocation: true metadata: disco-role: operating license: BSD 2-Clause

tslearn

Use this skill for the tslearn time-series machine-learning toolkit.

Start here when the task names tslearn, a tslearn example, a tslearn doc page, a tslearn error message, or a tslearn class/function such as TimeSeriesKMeans, dtw, LearningShapelets, VARIMA, or MatrixProfile.

Install and quick check

Public install:

python -m pip install tslearn

If you are working from a checkout and want an editable install instead:

python -m pip install -e .

Optional workflows may need extra packages:

• pandas for sktime / pyflux / tsfresh conversions
• stumpy for MatrixProfile stump
• h5py for HDF5 round-trips
• torch for the PyTorch metric backend and shapelet backend setup
• keras for LearningShapelets

Minimal import check:

python -c "import tslearn; print(tslearn.__version__)"

For a tiny environment check that also probes common optional packages, run:

python scripts/check_tslearn_env.py

Route map

If the user wants...	Go here
Load, shape, clean, resample, synchronize, symbolize, or convert time-series data	sub-skills/data-preparation/SKILL.md
DTW / Soft-DTW / GAK / LCSS / Fréchet / CTW, PyTorch backend choice, performance metrics, or barycenters	sub-skills/metrics-backends/SKILL.md
Cluster time series or score a clustering result	sub-skills/clustering/SKILL.md
Fit a k-NN, SVM, early-classification, shapelet, or time-series MLP model	sub-skills/supervised-models/SKILL.md
Fit or debug VARIMA / AutoVARIMA forecasting	sub-skills/forecasting/SKILL.md
Compute MatrixProfile or round-trip fitted estimators through JSON / Pickle / HDF5	sub-skills/analysis-and-persistence/SKILL.md

What this skill owns

tslearn uses dense time-series arrays shaped (n_ts, sz, d) and allows variable-length datasets by padding shorter series with trailing NaNs. The package spans:

• data utilities and interoperability helpers
• distance metrics and differentiable time-series losses
• barycenters and scoring helpers
• clustering estimators
• supervised models
• forecasting
• matrix profile
• fitted-estimator persistence

The module map and common optional dependencies live in references/overview.md.

When to read the root references

• references/overview.md — module ownership map and common dependency picture.
• references/troubleshooting.md — cross-cutting install/import/shape/backend/download/persistence failures.
• references/repo-provenance.md — check whether the skill still matches the current checkout.
• references/repo-routing-metadata.json — router metadata used during import/update.

Root-level guidance

1. If the request is broad or ambiguous, read references/overview.md first.
2. If the task crosses multiple workflow families, start with the most upstream sub-skill, then hand off downstream.
3. Prefer the bundled smoke helper only for a tiny environment sanity check.
4. Keep plotting gallery examples out of the runtime path; use the bundled smoke scripts instead.
5. Do not assume CUDA, stumpy, pandas, h5py, Keras, or torch are installed unless the task needs them.

Common handoffs

• Data shaping before modeling → data-preparation
• Metric choice before clustering or supervised modeling → metrics-backends
• Clustering after scaling / metric choice → clustering
• Supervised fitting after preprocessing and metric choice → supervised-models
• Forecasting after shaping the series → forecasting
• Analysis / persistence after fitting a model → analysis-and-persistence

Refresh check

Before using this skill on a different checkout, compare that checkout with references/repo-provenance.md. If the commit, branch, dirty state, package version, or evidence roots changed, refresh the skill instead of assuming it is current.