Welcome to pyts documentation!

pyts is a Python package dedicated to time series classification. It aims to make time series classification easily accessible by providing preprocessing and utility tools, and implementations of several time series classification algorithms. The package comes up with many unit tests and continuous integration ensures new code integration and backward compatibility. The package is distributed under the 3-clause BSD license.

Minimal example

The following code snippet illustrates the basic usage of pyts:

>>> from pyts.classification import BOSSVS
>>> from pyts.datasets import load_gunpoint
>>> X_train, X_test, y_train, y_test = load_gunpoint(return_X_y=True)
>>> clf = BOSSVS(window_size=28)
>>> clf.fit(X_train, y_train)
>>> clf.score(X_test, y_test)
  1. First we import:
  • a class defining a classifier (BOSSVS),
  • a function that loads the GunPoint dataset (load_gunpoint).
  1. Then we load the training and test sets by calling the load_gunpoint function.
  2. Next we define a classifier by creating an instance of the class.
  3. Finally we fit the classifier on the training set and evaluate its performance by computing the accuracy on the test set.

People familiar with scikit-learn API should feel comfortable with pyts as its API is heavily inspired from it, and pyts estimators are compatible with scikit-learn tools like model selection and pipelines. For more information, please refer to the Scikit-learn compatibility page.

Getting started

Information to install, test, and contribute to the package.

User Guide

The main documentation. This contains an in-depth description of all algorithms and how to apply them.

API Documentation

The exact API of all functions and classes, as given in the docstrings. The API documents expected types and allowed features for all functions, and all parameters available for the algorithms.


A set of examples illustrating the use of the different algorithms. It complements the User Guide.


History of notable changes to the pyts.

See the README for more information.

Indices and tables