TSFEL: Time Series Feature Extraction Library
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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.
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PDF URL: https://www.csl.uni-bremen.de/cms/images/documents/publications/TSFEL_SoftwareX2020.pdf
Reference:
TSFEL: Time Series Feature Extraction Library (Marília Barandas, Duarte Folgado, Letícia Fernandes, Sara Santos, Mariana Abreu, Patrícia Bota, Hui Liu, Tanja Schultz, Hugo Gamboa), In SoftwareX, Elsevier, volume 11, 2020.
Bibtex Entry:
@article{barandas2020tsfel,
  title = {{TSFEL}: Time Series Feature Extraction Library},
  author = {Barandas, Mar{\'\i}lia and Folgado, Duarte and Fernandes, Let{\'\i}cia and Santos, Sara and Abreu, Mariana and Bota, Patr{\'\i}cia and Liu, Hui and Schultz, Tanja and Gamboa, Hugo},
  journal = {SoftwareX},
  volume = {11},
  pages = {100456},
  year = {2020},
  publisher = {Elsevier},
  doi = {10.1016/j.softx.2020.100456},
  url={https://www.csl.uni-bremen.de/cms/images/documents/publications/TSFEL_SoftwareX2020.pdf},
  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.}
}