Journal
SOFTWAREX
Volume 11, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.softx.2020.100456
Keywords
Time series; Machine learning; Feature extraction; Python
Categories
Funding
- project Total Integrated and Predictive Manufacturing System Platform for Industry 4.0
- Portugal 2020, under the COMPETE 2020 (Operational Programme Competitiveness and Internationalisation)
- European Regional Development Fund (ERDF) from European Union (EU) [POCI-01-0247-FEDER-038436]
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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.
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