TSFEL: Time Series Feature Extraction Library

Barandas, Marília; Folgado, Duarte; Fernandes, Letícia; Santos, Sara; Abreu, Mariana; Bota, Patrícia; Liu, Hui; Schultz, Tanja; Gamboa, Hugo

http://hdl.handle.net/10362/117283

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Resumo(s)

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.

Descrição

POCI-01-0247-FEDER-038436

Palavras-chave

Feature extraction Machine learning Python Time series Software Computer Science Applications

URI

http://hdl.handle.net/10362/117283

DOI

10.1016/j.softx.2020.100456

Coleções

FCT: DF - Artigos em revista internacional com arbitragem científica

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