TSFEL: Time Series Feature Extraction Library
TSFEL: Time Series Feature Extraction Library
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DOI：
10.1016/j.softx.2020.100456
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发表时间：
2020-01-01
期刊：
影响因子：
3.4
通讯作者：
Gamboa, Hugo
中科院分区：
文献类型：
--
作者：
Barandas, Marilia;Folgado, Duarte;Gamboa, Hugo
时间序列特征提取是传统机器学习的基本步骤之一。通常，这个过程是一项耗时且复杂的任务，因为数据科学人员必须考虑多种领域知识因素和编码实现之间的组合。本文介绍了一个名为时间序列特征提取库(TSFEL)的Python包，它计算了60多个跨时间域、统计域和谱域提取的不同特征。用户定制是使用在线界面或传统的Python包实现的，以获得更大的灵活性并集成到实际部署场景中。TSFEL的设计目的是支持对时间序列进行快速探索性数据分析和特征提取，并进行计算成本评估。(C)2020作者。爱思唯尔出版公司(Elsevier B.V.)
Time series feature extraction is one of the preliminary steps of conventional machine learning pipelines. Quite often, this process ends being a time consuming and complex task as data scien-tists must consider a combination between a multitude of domain knowledge factors and coding implementation. We present in this paper a Python package entitled Time Series Feature Extraction Library (TSFEL), which computes over 60 different features extracted across temporal, statistical and spectral domains. User customisation is achieved using either an online interface or a conventional Python package for more flexibility and integration into real deployment scenarios. TSFEL is designed to support the process of fast exploratory data analysis and feature extraction on time series with computational cost evaluation. (C) 2020 The Authors. Published by Elsevier B.V.