tslearn-team/tslearn

Health v2 · maintenance only

Adoption not part of the score

3174 stars · 380 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

tslearn is a Python machine learning library dedicated to time series analysis, built on numpy and compatible with scikit-learn. It provides tools for time series classification, clustering, and preprocessing, including Dynamic Time Warping (DTW) based algorithms.

Use cases

• cluster time series with DTW-based k-means
• classify time series with nearest neighbors using dynamic time warping
• preprocess and resample time series datasets in Python
• compute DTW barycenter averaging for time series
• run scikit-learn-style machine learning on variable-length time series
• benchmark time series classification algorithms

When to choose

• you need dedicated time series ML algorithms like DTW, DTWI, or global alignment kernels in Python
• you want scikit-learn-compatible estimators for time series tasks
• you work with variable-length or multivariate time series datasets

When to avoid

• you need general-purpose tabular machine learning rather than time series
• you need deep learning models for time series (consider sktime, darts, or PyTorch)
• you need streaming or real-time time series processing

Facets

library · maturity stable

machine-learning data-science machine-learning data-science time-series python cross-platform time-series dtw dynamic-time-warping clustering classification scikit-learn-compatible

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markdown · JSON · MCP: product_card(name="tslearn-team/tslearn")