# johannfaouzi/pyts

A Python package for time series classification

Repository: https://github.com/johannfaouzi/pyts
Canonical: https://ross.abutalabs.com/products/pyts
Homepage: https://pyts.readthedocs.io
Language: Python
License: BSD-3-Clause
License Family: permissive
Topics: python, classification, machine-learning, time-series-classification, time-series-analysis, time-series
Last push: 2025-06-18T15:41:19+00:00

## Health v2 (maintenance only)
Score: 35/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 27, release rhythm 8, longevity 100
- inputs: {"age_days": 3320, "days_push": 441, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1876, forks 179 (observed 2026-08-28T04:05:47.725568+00:00)

## What it is
pyts is a Python package for time series classification that provides preprocessing, transformation, and utility tools along with implementations of state-of-the-art algorithms. It follows scikit-learn conventions and uses Numba for performance optimization.

## Use cases
- classify time series data
- transform time series for machine learning
- run state-of-the-art time series classification algorithms
- preprocess sensor or signal data for classification
- benchmark time series classifiers on UCR datasets
- integrate time series classification into scikit-learn pipelines

## When to choose
- you need a scikit-learn-compatible library for time series classification
- you want access to many classic and modern TSC algorithms in one package
- you work in Python with NumPy/SciPy and want easy pip/conda installation
- you need well-documented tools for time series transformations like SAX, PAA, or BOSS

## When to avoid
- you need time series forecasting or regression rather than classification
- you need deep-learning-based time series models (consider sktime, tsai, or PyTorch)
- you need streaming or online time series classification
- you work outside the Python ecosystem

## Facets
- artifact type: library
- maturity: stable
- function: machine-learning, data-science, etl
- domain: machine-learning, data-science, time-series
- platform: python, cross-platform
- tags: time-series-classification, scikit-learn-compatible, numba, signal-processing

## Member repositories
- johannfaouzi/pyts (main) score 35

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:47.725568+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 2026-08-30T03:14:44.540197+00:00, confidence not recorded.
  - readme: https://github.com/johannfaouzi/pyts (fetched 2026-08-28T04:05:47.725568+00:00, sha 78b735f61bee)
  - registry_pypi: https://pypi.org/pypi/pyts/json (fetched 2026-08-29T10:54:10.263356+00:00, sha 328c41d4f08a)
- Data as of 2026-08-30T08:39:29.467469+00:00.