TSFEL swMATH ID: 43033 Software Authors: Barandas M, Folgado D, Fernandes L, Santos S, Abreu M, Bota P, Liu H, Schultz T, Gamboa H Description: TSFEL: Time Series Feature Extraction Library. 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. Homepage: https://tsfel.readthedocs.io/en/latest/ Source Code: https://github.com/fraunhoferportugal/tsfel Dependencies: Python Keywords: SoftwareX; TSFEL; Time Series; Feature Extraction; Python; Machine learning Related Software: tsfresh; Kats; NumPy; Python; hctsa; WESAD; Seglearn; Scikit; Statsmodels; Viztracer; tsflex; SciPy; pandas; Timbre Toolbox; MLxtend; cesium; FATS; tidyverse; t-SNE; FFORMA Cited in: 1 Publication Standard Articles 1 Publication describing the Software Year Cited by 4 Authors 1 Achenchabe, Youssef 1 Bondu, Alexis 1 Cornuéjols, Antoine 1 Dachraoui, Asma Cited in 1 Serial 1 Machine Learning Cited in 1 Field 1 Computer science (68-XX) Citations by Year