Seasonal-Trend Time Series Decomposition on Graphics Processing Units

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

In many domains, large amounts of time series data are being collected and analyzed in a semi-automatic manner. A prominent approach is the seasonal and trend decomposition using locally estimated scatterplot smoothing (STL) technique, which has been applied extensively in the past. However, STL quickly becomes computationally very expensive when applied to large data sets. In this work, we propose the first parallel implementation for the STL decomposition approach, which is tailored to the specific needs of graphics processing units (GPU). Our experimental evaluation on two global-scale case studies in temperature and vegetation trend analysis exhibits at least three-to-four orders of magnitude speed-up, demonstrating the effectiveness of the overall approach and the immense potential of the implementation in spatio-temporal data analyses. The source code is publicly available at An artifact that allows the experimental results to be reproduced is available at FfA.

Original languageEnglish
Title of host publicationProceedings - 2023 IEEE International Conference on Big Data, BigData 2023
EditorsJingrui He, Themis Palpanas, Xiaohua Hu, Alfredo Cuzzocrea, Dejing Dou, Dominik Slezak, Wei Wang, Aleksandra Gruca, Jerry Chun-Wei Lin, Rakesh Agrawal
Number of pages10
Publication date2023
ISBN (Electronic)9798350324457
Publication statusPublished - 2023
Event2023 IEEE International Conference on Big Data, BigData 2023 - Sorrento, Italy
Duration: 15 Dec 202318 Dec 2023


Conference2023 IEEE International Conference on Big Data, BigData 2023
SponsorAnkura, IEEE Dataport

Bibliographical note

Funding Information:
This work has been supported by the Independent Research Fund Denmark (DFF) under the grant: High-performance Architectures and Monitoring Changes in Big Satellite Data via Massively Parallel AI, and by the UCPH Data+ grant: High-Performance Land Change Assessment.

Publisher Copyright:
© 2023 IEEE.

    Research areas

  • Climate Change, Parallel Implementation, Remote Sensing, Time Series Data, Trend Analysis

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