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2020 JMLR JMLR 2020

Tslearn, A Machine Learning Toolkit for Time Series Data

Abstract

tslearn is a general-purpose Python machine learning library for time series that offers tools for pre-processing and feature extraction as well as dedicated models for clustering, classification and regression. It follows scikit-learn's Application Programming Interface for transformers and estimators, allowing the use of standard pipelines and model selection tools on top of tslearn objects. It is distributed under the BSD-2-Clause license, and its source code is available at https://github.com/tslearn-team/tslearn. [abs] [ pdf ][ bib ] [ code ] © JMLR 2020. (edit, beta)

🧭 Keyword Pioneer — time series regression
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Speech & Audio
🌉 Interdisciplinary Bridge — Data Science & Analytics and Machine Learning

Authors

Romain Tavenard , Johann Faouzi , Gilles Vandewiele , Felix Divo , Guillaume Androz , Chester Holtz , Marie Payne , Roman Yurchak , Marc Rußwurm , Kushal Kolar , Eli Woods

Topics

Machine Learning > Core Methods > Classification Machine Learning > Core Methods > Clustering Machine Learning > Core Methods > Regression Data Science & Analytics > Methods > Time Series

Keywords

feature extraction time series clustering time series classification time series regression time series preprocessing machine learning toolkit
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