TSFEL: Time Series Feature Extraction Library

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

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)
31 Downloads (Pure)

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.

Original languageEnglish
Article number100456
JournalSoftwareX
Volume11
DOIs
Publication statusPublished - 1 Jan 2020

Keywords

  • Feature extraction
  • Machine learning
  • Python
  • Time series

Fingerprint Dive into the research topics of 'TSFEL: Time Series Feature Extraction Library'. Together they form a unique fingerprint.

Cite this