Pyts: A python package for time series classification

被引：0

作者：

Faouzi, Johann ^{[1
]}

Janati, Hicham ^{[2
]}

机构：

[1] Aramis Lab, INRIA Paris Brain and Spine Institute, Paris,75013, France

[2] INRIA Saclay Neurospin, Bât 145 CEA Saclay, Gif sur Yvette,91191, France

来源：

Journal of Machine Learning Research | 2020年 / 21卷

关键词：

Open source software - High level languages - Application programming interfaces (API) - Machine learning - Time series;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

Pyts is an open-source Python package for time series classification. This versatile toolbox provides implementations of many algorithms published in the literature, preprocessing functionalities, and data set loading utilities. pyts relies on the standard scientific Python packages numpy, scipy, scikit-learn, joblib, and numba, and is distributed under the BSD-3-Clause license. Documentation contains installation instructions, a detailed user guide, a full API description, and concrete self-contained examples. Source code and documentation can be downloaded from https://github.com/johannfaouzi/pyts. © 2020 Microtome Publishing. All rights reserved.

引用

共 50 条

[1] pyts: A Python']Python Package for Time Series Classification
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[J]. JOURNAL OF MACHINE LEARNING RESEARCH, 2020, 21
[2] Seglearn: A Python']Python Package for Learning Sequences and Time Series
Burns, David M.
Whyne, Cari M.
[J]. JOURNAL OF MACHINE LEARNING RESEARCH, 2018, 19
[3] dtwParallel: A Python']Python package to efficiently compute dynamic time warping between time series
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[4] MarineTools.temporal: A Python']Python package to simulate Earth and environmental time series
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[5] STMETRICS: A PYTHON']PYTHON PACKAGE FOR SATELLITE IMAGE TIME-SERIES FEATURE EXTRACTION
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Neves, Alana K.
Lechler, Sarah
Korting, Thales S.
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