# wannesm/dtaidistance

Time series distances: Dynamic Time Warping (fast  DTW implementation in C)

Repository: https://github.com/wannesm/dtaidistance
Canonical: https://ross.abutalabs.com/products/dtaidistance
Language: Python
License: NOASSERTION
License Family: other
Topics: timeseries, dtw, clustering, dynamic-time-warping, distance-measure, c, python
Last push: 2026-07-07T14:32:41+00:00

## Health v2 (maintenance only)
Score: 64/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 91, release rhythm 8, longevity 100
- inputs: {"age_days": 3499, "days_push": 57, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1240, forks 191 (observed 2026-08-28T04:04:06.149061+00:00)

## What it is
A Python library for computing time series distances, most notably Dynamic Time Warping (DTW), with a fast C implementation exposed via Cython. It supports N-dimensional series, parallelized distance matrix computation, DTW Barycenter Averaging for clustering, subsequence search, and local concurrences.

## Use cases
- compute dtw distance between two time series
- compute pairwise dtw distance matrix for many series fast
- cluster time series with dtw barycenter averaging
- find best matching subsequence in a longer series
- compare multivariate time series with dtw
- speed up dtw computation with pruning

## When to choose
- you need fast, C-accelerated DTW in Python with minimal dependencies
- you want DTW-based clustering or subsequence search for time series
- you work with numpy/pandas time series and need efficient distance matrices

## When to avoid
- you need many other time series similarity measures beyond DTW variants
- you need a pure non-Python solution or GPU-accelerated DTW
- you need full time series forecasting or classification pipelines rather than distances

## Facets
- artifact type: library
- maturity: stable
- function: math, machine-learning, data-science
- domain: time-series, machine-learning, data-science
- platform: python, cross-platform
- tags: dtw, time-series-distances, clustering, cython, numpy, dynamic-time-warping, algorithms

## Member repositories
- wannesm/dtaidistance (main) score 64

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:06.149061+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 2026-08-30T05:09:55.014589+00:00, confidence not recorded.
  - readme: https://github.com/wannesm/dtaidistance (fetched 2026-08-28T04:04:06.149061+00:00, sha 3369dbd572d3)
  - registry_pypi: https://pypi.org/pypi/dtaidistance/json (fetched 2026-08-29T12:20:51.273318+00:00, sha 2b37c327f05a)
- Data as of 2026-08-30T08:39:29.467469+00:00.