Seasonal-Trend Time Series Decomposition on Graphics Processing Units
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
Standard
Seasonal-Trend Time Series Decomposition on Graphics Processing Units. / Serykh, Dmitry; Oehmcke, Stefan; Oancea, Cosmin; Masiliunas, Dainius; Verbesselt, Jan; Cheng, Yan; Horion, Stephanie; Gieseke, Fabian; Hinnerskov, Nikolaj.
Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023. red. / Jingrui He; Themis Palpanas; Xiaohua Hu; Alfredo Cuzzocrea; Dejing Dou; Dominik Slezak; Wei Wang; Aleksandra Gruca; Jerry Chun-Wei Lin; Rakesh Agrawal. IEEE, 2023. s. 5914-5923.Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
TY - GEN
T1 - Seasonal-Trend Time Series Decomposition on Graphics Processing Units
AU - Serykh, Dmitry
AU - Oehmcke, Stefan
AU - Oancea, Cosmin
AU - Masiliunas, Dainius
AU - Verbesselt, Jan
AU - Cheng, Yan
AU - Horion, Stephanie
AU - Gieseke, Fabian
AU - Hinnerskov, Nikolaj
N1 - 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.
PY - 2023
Y1 - 2023
N2 - 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 https://github.com/diku-dk/hastl. An artifact that allows the experimental results to be reproduced is available at https://sid.erda.dk/sharelink/hOUrqJJ FfA.
AB - 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 https://github.com/diku-dk/hastl. An artifact that allows the experimental results to be reproduced is available at https://sid.erda.dk/sharelink/hOUrqJJ FfA.
KW - Climate Change
KW - Parallel Implementation
KW - Remote Sensing
KW - Time Series Data
KW - Trend Analysis
U2 - 10.1109/BigData59044.2023.10386208
DO - 10.1109/BigData59044.2023.10386208
M3 - Article in proceedings
AN - SCOPUS:85184984790
SP - 5914
EP - 5923
BT - Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023
A2 - He, Jingrui
A2 - Palpanas, Themis
A2 - Hu, Xiaohua
A2 - Cuzzocrea, Alfredo
A2 - Dou, Dejing
A2 - Slezak, Dominik
A2 - Wang, Wei
A2 - Gruca, Aleksandra
A2 - Lin, Jerry Chun-Wei
A2 - Agrawal, Rakesh
PB - IEEE
T2 - 2023 IEEE International Conference on Big Data, BigData 2023
Y2 - 15 December 2023 through 18 December 2023
ER -
ID: 385219280