点击次数：

影响因子：3.4

所属单位：University of Bremen

发表刊物：SoftwareX

刊物所在地：Netherlands

关键字：time series; machine learning; feature extraction; Python

摘要：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 scientists 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.

备注：Codebank: https://github.com/fraunhoferportugal/tsfel.
Documentation: https://tsfel.readthedocs.io/en/latest/

全部作者：Marília Barandas*, Duarte Folgado, Letícia Fernandes, Sara Santos, Mariana Abreua, Patrícia Bota, Hui Liu, Tanja Schultz, Hugo Gamboa

论文类型：期刊论文

学科门类：工学

文献类型：J

卷号：11

页面范围：100456

是否译文：否

发表时间：2020-06-01

收录刊物：SCI