# tslearn-team/tslearn

The machine learning toolkit for time series analysis in Python

Repository: https://github.com/tslearn-team/tslearn
Canonical: https://ross.abutalabs.com/products/tslearn
Homepage: https://tslearn.readthedocs.io
Language: Python
License: BSD-2-Clause
License Family: permissive
Topics: time-series, timeseries, time-series-analysis, time-series-clustering, machine-learning, machine-learning-algorithms, machinelearning, dtw, python, time-series-classification, dynamic-time-warping
Last push: 2026-08-26T15:32:09+00:00

## Health v2 (maintenance only)
Score: 88/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 66, longevity 100
- inputs: {"age_days": 3408, "days_push": 7, "days_rel": 64, "gap_med": 103.0, "n_releases_24m": 5}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3174, forks 380 (observed 2026-08-28T04:07:47.571820+00:00)

## What it is
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
- artifact type: library
- maturity: stable
- function: machine-learning, data-science
- domain: machine-learning, data-science, time-series
- platform: python, cross-platform
- tags: time-series, dtw, dynamic-time-warping, clustering, classification, scikit-learn-compatible

## Member repositories
- tslearn-team/tslearn (main) score 88

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:47.571820+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:45:26.792045+00:00, confidence not recorded.
  - readme: https://github.com/tslearn-team/tslearn (fetched 2026-08-28T04:07:47.571820+00:00, sha 0107c0ea8f53)
  - registry_pypi: https://pypi.org/pypi/tslearn/json (fetched 2026-08-29T09:40:01.842670+00:00, sha c6327cef6297)
- Data as of 2026-08-30T08:39:29.467469+00:00.