[TSFEL Codebank] TSFEL: Time Series Feature Extraction Library
Date:2025-05-23 Hits:
Impact Factor:3.4
Affiliation of Author(s):University of Bremen
Journal:SoftwareX
Place of Publication:Netherlands
Key Words:time series; machine learning; feature extraction; Python
Abstract: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.
Note:Codebank: https://github.com/fraunhoferportugal/tsfel.
Documentation: https://tsfel.readthedocs.io/en/latest/
All the Authors:Marília Barandas*, Duarte Folgado, Letícia Fernandes, Sara Santos, Mariana Abreua, Patrícia Bota, Hui Liu, Tanja Schultz, Hugo Gamboa
Indexed by:Journal paper
Discipline:Engineering
Document Type:J
Volume:11
Page Number:100456
Translation or Not:no
Date of Publication:2020-06-01
Included Journals:SCI