# stumpy-dev/stumpy

STUMPY is a powerful and scalable Python library for modern time series analysis

Repository: https://github.com/stumpy-dev/stumpy
Canonical: https://ross.abutalabs.com/products/stumpy
Homepage: https://stumpy.readthedocs.io/en/latest/
Language: Python
License: NOASSERTION
License Family: other
Topics: data-science, time-series-analysis, dask, numba, python, anomaly-detection, pattern-matching, pydata, matrix-profile, motif-discovery, time-series-segmentation, time-series-data-mining
Last push: 2026-08-26T02:38:40+00:00

## Health v2 (maintenance only)
Score: 89/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 99, release rhythm 69, longevity 100
- inputs: {"age_days": 2679, "days_push": 8, "days_rel": 207, "gap_med": 4, "n_releases_24m": 2}
- 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 4148, forks 367 (observed 2026-08-28T04:08:36.430832+00:00)

## What it is
STUMPY is a Python library that efficiently computes the matrix profile for time series, enabling fast nearest-neighbor subsequence searches. It supports time series data mining tasks such as motif discovery, anomaly (discord) detection, and semantic segmentation, with scalable GPU and distributed (Dask) backends.

## Use cases
- find repeated patterns (motifs) in a long time series
- detect anomalies or discords in sensor data
- compute the matrix profile for time series
- segment a time series into semantic regions
- mine time series data at scale with Dask or GPUs
- find nearest-neighbor subsequences in streaming data

## When to choose
- you need efficient motif or anomaly discovery in univariate or multivariate time series
- you want a well-tested, NumFOCUS-affiliated library with 100% test coverage
- you need scalability via Numba, GPU, or Dask parallelization

## When to avoid
- you need general-purpose forecasting or classical time series models like ARIMA
- you work outside Python
- you need real-time streaming analytics with sub-millisecond latency guarantees

## Facets
- artifact type: library
- maturity: stable
- function: data-science, math
- domain: data-science, time-series, analytics
- platform: python, cross-platform
- tags: matrix-profile, time-series-analysis, motif-discovery, anomaly-detection, numba, dask

## Member repositories
- stumpy-dev/stumpy (main) score 89

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:36.430832+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 2026-08-29T18:22:55.075015+00:00, confidence not recorded.
  - readme: https://github.com/stumpy-dev/stumpy (fetched 2026-08-28T04:08:36.430832+00:00, sha 51ba84818eda)
  - registry_pypi: https://pypi.org/pypi/stumpy/json (fetched 2026-08-29T09:14:22.209279+00:00, sha c976fc55ca90)
- Data as of 2026-08-30T08:39:29.467469+00:00.