Welcome to sktime#

A unified framework for machine learning with time series.


sktime provides an easy-to-use, flexible and modular open-source framework for a wide range of time series machine learning tasks. It offers scikit-learn compatible interfaces and model composition tools, with the goal to make the ecosystem more usable and interoperable as a whole. We build and sustain an open, diverse and self-governing community, welcoming new contributors from academia and industry through instructive documentation, mentoring and workshops.


sktime features a unified interface for multiple time series learning tasks. Currently, we support forecasting, time series classification, time series regression and time series clustering. We have experimental support for time series annotation.


  • API for machine learning with time series, for the purpose of specifying, fitting, applying and validating machine learning models

  • Interactive user experience with scikit-learn like syntax conventions

Technical specification:

  • In-memory computation of a single machine, no distributed computing

  • Medium-sized data in pandas and NumPy

  • Modular, principled and object-oriented API

  • Making use of interactive Python interpreter, no command-line interface or graphical user interface


Get Started

Get started using sktime quickly.

User Guide

Find user documentation.


Installation Guide.

API Reference

Understand sktime’s API.

Get Involved

Find out how you can contribute.


Information for developers.


Learn more about sktime.