Academic Bibliography

 Search 200 years of publications by Ghent University researchers.
Advanced search
1 file | 669.91 KB
Add to list
  1. Search results
  2. tsflex : flexible time series process...

tsflex : flexible time series processing & feature extraction

Jonas Van Der Donckt (UGent) , Jeroen Van Der Donckt (UGent) , Emiel Deprost (UGent) and Sofie Van Hoecke (UGent)
(2022) SOFTWAREX. 17.
Author
Organization
Project
Abstract
Time series processing and feature extraction are crucial and time-intensive steps in conventional machine learning pipelines. Existing packages are limited in their applicability, as they cannot cope with irregularly-sampled or asynchronous data and make strong assumptions about the data format. Moreover, these packages do not focus on execution speed and memory efficiency, resulting in considerable overhead. We present tsflex, a Python toolkit for time series processing and feature extraction, that focuses on performance and flexibility, enabling broad applicability. This toolkit leverages window-stride arguments of the same data type as the sequence-index, and maintains the sequence-index through all operations. tsflex is flexible as it supports (1) multivariate time series, (2) multiple window-stride configurations, and (3) integrates with processing and feature functions from other packages, while (4) making no assumptions about the data sampling regularity, series alignment, and data type. Other functionalities include multiprocessing, detailed execution logging, chunking sequences, and serialization. Benchmarks show that tsflex is faster and more memory-efficient compared to similar packages, while being more permissive and flexible in its utilization. (C) 2022 The Author(s). Published by Elsevier B.V.
Keywords
Time series, Processing, Feature extraction, Machine learning, Python

Downloads

  • Download
    DS496.pdf
    • full text (Published version)
    • |
    • open access
    • |
    • PDF
    • |
    • 669.91 KB

Citation

Please use this url to cite or link to this publication:

MLA
Van Der Donckt, Jonas, et al. “Tsflex : Flexible Time Series Processing & Feature Extraction.” SOFTWAREX, vol. 17, 2022, doi:10.1016/j.softx.2021.100971.
APA
Van Der Donckt, J., Van Der Donckt, J., Deprost, E., & Van Hoecke, S. (2022). tsflex : flexible time series processing & feature extraction. SOFTWAREX, 17. https://doi.org/10.1016/j.softx.2021.100971
Chicago author-date
Van Der Donckt, Jonas, Jeroen Van Der Donckt, Emiel Deprost, and Sofie Van Hoecke. 2022. “Tsflex : Flexible Time Series Processing & Feature Extraction.” SOFTWAREX 17. https://doi.org/10.1016/j.softx.2021.100971.
Chicago author-date (all authors)
Van Der Donckt, Jonas, Jeroen Van Der Donckt, Emiel Deprost, and Sofie Van Hoecke. 2022. “Tsflex : Flexible Time Series Processing & Feature Extraction.” SOFTWAREX 17. doi:10.1016/j.softx.2021.100971.
Vancouver
1.
Van Der Donckt J, Van Der Donckt J, Deprost E, Van Hoecke S. tsflex : flexible time series processing & feature extraction. SOFTWAREX. 2022;17.
IEEE
[1]
J. Van Der Donckt, J. Van Der Donckt, E. Deprost, and S. Van Hoecke, “tsflex : flexible time series processing & feature extraction,” SOFTWAREX, vol. 17, 2022.
@article{8748221,
  abstract     = {{Time series processing and feature extraction are crucial and time-intensive steps in conventional machine learning pipelines. Existing packages are limited in their applicability, as they cannot cope with irregularly-sampled or asynchronous data and make strong assumptions about the data format. Moreover, these packages do not focus on execution speed and memory efficiency, resulting in considerable overhead. We present tsflex, a Python toolkit for time series processing and feature extraction, that focuses on performance and flexibility, enabling broad applicability. This toolkit leverages window-stride arguments of the same data type as the sequence-index, and maintains the sequence-index through all operations. tsflex is flexible as it supports (1) multivariate time series, (2) multiple window-stride configurations, and (3) integrates with processing and feature functions from other packages, while (4) making no assumptions about the data sampling regularity, series alignment, and data type. Other functionalities include multiprocessing, detailed execution logging, chunking sequences, and serialization. Benchmarks show that tsflex is faster and more memory-efficient compared to similar packages, while being more permissive and flexible in its utilization. (C) 2022 The Author(s). Published by Elsevier B.V.}},
  articleno    = {{100971}},
  author       = {{Van Der Donckt, Jonas and Van Der Donckt, Jeroen and Deprost, Emiel and Van Hoecke, Sofie}},
  issn         = {{2352-7110}},
  journal      = {{SOFTWAREX}},
  keywords     = {{Time series,Processing,Feature extraction,Machine learning,Python}},
  language     = {{eng}},
  pages        = {{6}},
  title        = {{tsflex : flexible time series processing & feature extraction}},
  url          = {{http://dx.doi.org/10.1016/j.softx.2021.100971}},
  volume       = {{17}},
  year         = {{2022}},
}
Altmetric
View in Altmetric
Web of Science
Times cited: