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A Python package for time series classification
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README.md

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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.

Installation

Dependencies

pyts requires:

  • Python (>= 3.5)
  • NumPy (>= 1.15.4)
  • SciPy (>= 1.3.0)
  • Scikit-Learn (>=0.20.4)
  • Joblib (>=0.12)
  • Numba (>=0.45.1)

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 https://github.com/johannfaouzi/pyts.git
cd pyts
pip install .

Testing

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

pytest pyts

Changelog

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

Development

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.

Documentation

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:

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