TSFEL: Time Series Feature Extraction Library

By Marília Barandas, Duarte Folgado, Letícia Fernandes, Sara Santos, Mariana Abreu, Patrícia Bota, Hui Liu, Tanja Schultz, H. Gamboa. SoftwareX, 2020

485 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Economics and Econometrics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/30d86ed68c163455b8f3ef10a5bf4c92ee8707bc

Citations in the corpus

• Data-centric Artificial Intelligence: A Survey (2023, unknown): “For time-series data, temporal, statistical, and spectral features are often considered [13].”
• Text embedding models can be great data engineers (2025, unknown): “We take this opportunity to test the performance of the existing AutoML methods, specifically the TSFEL package [4].”