Welcome to sktime#
A unified framework for machine learning with time series.
sktime Fall dev days#
Please join the sktime community Nov 9 - 10 2022 for our virtual Fall dev days. There will be an additional in-person social option in regions with enough participants. Our core hours will be 10-12 UTC, with additional meeting before/after these core hours! This is a great opportunity for new community members to learn more about how to contribute to sktime as well as a good opportunity for existing members to share what we have been up to! Please register here!
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
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
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Find user documentation.
Understand sktime’s API.
Find out how you can contribute.
Information for developers.
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