Seglearn: A Python']Python Package for Learning Sequences and Time Series

被引:0
|
作者
Burns, David M. [1 ]
Whyne, Cari M. [1 ]
机构
[1] Sunnybrook Res Inst, 2075 Bayview Ave,Room S620, Toronto, ON M4N 3M5, Canada
关键词
Machine-Learning; Time-Series; Sequences; !text type='Python']Python[!/text;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
seglearn is an open-source Python package for performing machine learning on time series or sequences. The implementation provides a flexible pipeline for tackling classification, regression, and forecasting problems with multivariate sequence and contextual data. Sequences and series may be learned directly with deep learning models or via feature representation with classical machine learning estimators. This package is compatible with scikit-learn and is listed under scikit-learn "Related Projects". The package depends on numpy, scipy, and scikit-learn. seglearn is distributed under the BSD 3-Clause License. Documentation includes a detailed API description, user guide, and examples. Unit tests provide a high degree of code coverage. Source code and documentation can be downloaded from https://github.com/dmbee/seglearn.
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页数:7
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