Awesome Open Source
Awesome Open Source

Build Status Documentation Status Codecov PyPI - Python Version PyPI version Conda Version Language grade: Python Gitter DOI

pyts: a Python package for time series classification

pyts is a Python package for time series classification. It aims to make time series classification easily accessible by providing preprocessing and utility tools, and implementations of state-of-the-art algorithms. Most of these algorithms transform time series, thus pyts provides several tools to perform these transformations.



pyts requires:

  • Python (>= 3.6)
  • NumPy (>= 1.17.5)
  • SciPy (>= 1.3.0)
  • Scikit-Learn (>=0.22.1)
  • Joblib (>=0.12)
  • Numba (>=0.48.0)

To run the examples Matplotlib (>=2.0.0) is required.

User installation

If you already have a working installation of numpy, scipy, scikit-learn, joblib and numba, you can easily install pyts using pip

pip install pyts

or conda via the conda-forge channel

conda install -c conda-forge pyts

You can also get the latest version of pyts by cloning the repository

git clone
cd pyts
pip install .


After installation, you can launch the test suite from outside the source directory using pytest:

pytest pyts


See the changelog for a history of notable changes to pyts.


The development of this package is in line with the one of the scikit-learn community. Therefore, you can refer to their Development Guide. A slight difference is the use of Numba instead of Cython for optimization.


The section below gives some information about the implemented algorithms in pyts. For more information, please have a look at the HTML documentation available via ReadTheDocs.


If you use pyts in a scientific publication, we would appreciate citations to the following paper:

Johann Faouzi and Hicham Janati. pyts: A python package for time series classification.
Journal of Machine Learning Research, 21(46):1−6, 2020.

Bibtex entry:

  author  = {Johann Faouzi and Hicham Janati},
  title   = {pyts: A Python Package for Time Series Classification},
  journal = {Journal of Machine Learning Research},
  year    = {2020},
  volume  = {21},
  number  = {46},
  pages   = {1-6},
  url     = {}

Implemented features

Note: the content described in this section corresponds to the main branch, not the latest released version. You may have to install the latest version to use some of these features.

pyts consists of the following modules:

Get A Weekly Email With Trending Projects For These Topics
No Spam. Unsubscribe easily at any time.
python (54,447
machine-learning (3,642
classification (282
timeseries (103