Welcome to sktime#

A unified framework for machine learning with time series.

Applications for sktime internships 2023 open!

Application deadline is May 19. Apply here

Mission#

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.

Features#

unified API for machine learning with time series, for model specification, fitting, application, and validation
supported tasks include forecasting, time series classification, time series regression, time series clustering.
tools for composite model buildin including pipelining with transformations, ensembling, tuning and reduction
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 based containers
Modular, principled and object-oriented API
Using interactive Python interpreter, no command-line interface or graphical user interface