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Apr. 22, 2019
Jul. 31, 2017

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pyts: a Python package for time series transformation and classification

pyts is a Python package for time series transformation and 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 (>= 3.5)
  • NumPy (>= 1.15.4)
  • SciPy (>= 1.1.0)
  • Scikit-Learn (>=0.20.1)
  • Numba (>=0.41.0)

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

User installation

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

pip install 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.

Implemented features

pyts consists of the following modules:

Latest Releases
Release of 0.7.3 version
 Feb. 11 2019
pyts 0.7.0
 May. 22 2018
 May. 18 2018
First release of pyts
 May. 9 2018