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info.json
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{
"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.",
"authors": [
"Romain Tavenard",
"Johann Faouzi",
"Gilles Vandewiele",
"Felix Divo",
"Guillaume Androz",
"Chester Holtz",
"Marie Payne",
"Roman Yurchak",
"Marc Ru\u00dfwurm",
"Kushal Kolar",
"Eli Woods"
],
"emails": [
"romain.tavenard@univ-rennes2.fr",
"johann.faouzi@icm-institute.org",
"gilles.vandewiele@ugent.be",
"felix.divo@stud.tu-darmstadt.de",
"guillaume.androz@icentia.com",
"chholtz@eng.ucsd.edu",
"marie.payne@mail.mcgill.ca",
"roman.yurchak@symerio.com",
"marc.russwurm@tum.de",
"kushalkolar@gmail.com",
"eli@eaze.com"
],
"extra_links": [
[
"code",
"https://github.com/tslearn-team/tslearn"
]
],
"id": "20-091",
"issue": 118,
"pages": [
1,
6
],
"special_issue": "MLOSS",
"title": "Tslearn, A Machine Learning Toolkit for Time Series Data",
"volume": 21,
"year": 2020
}