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作者:

Wang, Shun (Wang, Shun.) | Zhang, Yong (Zhang, Yong.) (学者:张勇) | Lin, Xuanqi (Lin, Xuanqi.) | Hu, Yongli (Hu, Yongli.) | Huang, Qingming (Huang, Qingming.) | Yin, Baocai (Yin, Baocai.)

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EI Scopus SCIE

摘要:

Multivariate time series forecasting plays an important role in many domain applications, such as air pollution forecasting and traffic forecasting. Modeling the complex dependencies among time series is a key challenging task in multivariate time series forecasting. Many previous works have used graph structures to learn inter-series correlations, which have achieved remarkable performance. However, graph networks can only capture spatio-temporal dependencies between pairs of nodes, which cannot handle high-order correlations among time series. We propose a Dynamic Hypergraph Structure Learning model (DHSL) to solve the above problems. We generate dynamic hypergraph structures from time series data using the K-Nearest Neighbors method. Then a dynamic hypergraph structure learning module is used to optimize the hypergraph structure to obtain more accurate high-order correlations among nodes. Finally, the hypergraph structures dynamically learned are used in the spatio-temporal hypergraph neural network. We conduct experiments on six real-world datasets. The prediction performance of our model surpasses existing graph network-based prediction models. The experimental results demonstrate the effectiveness and competitiveness of the DHSL model for multivariate time series forecasting.

关键词:

Recurrent neural networks Time series analysis hypergraph structure learning Forecasting Predictive models multivariate time series forecasting Data models Adaptation models Graph neural network Correlation

作者机构:

  • [ 1 ] [Wang, Shun]Beijing Univ Technol, Beijing Artificial Intelligence Inst, Fac Informat Technol, Beijing Key Lab Multimedia & Intelligent Software, Beijing 100124, Peoples R China
  • [ 2 ] [Zhang, Yong]Beijing Univ Technol, Beijing Artificial Intelligence Inst, Fac Informat Technol, Beijing Key Lab Multimedia & Intelligent Software, Beijing 100124, Peoples R China
  • [ 3 ] [Lin, Xuanqi]Beijing Univ Technol, Beijing Artificial Intelligence Inst, Fac Informat Technol, Beijing Key Lab Multimedia & Intelligent Software, Beijing 100124, Peoples R China
  • [ 4 ] [Hu, Yongli]Beijing Univ Technol, Beijing Artificial Intelligence Inst, Fac Informat Technol, Beijing Key Lab Multimedia & Intelligent Software, Beijing 100124, Peoples R China
  • [ 5 ] [Huang, Qingming]Beijing Univ Technol, Beijing Artificial Intelligence Inst, Fac Informat Technol, Beijing Key Lab Multimedia & Intelligent Software, Beijing 100124, Peoples R China
  • [ 6 ] [Yin, Baocai]Beijing Univ Technol, Beijing Artificial Intelligence Inst, Fac Informat Technol, Beijing Key Lab Multimedia & Intelligent Software, Beijing 100124, Peoples R China
  • [ 7 ] [Huang, Qingming]Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100049, Peoples R China

通讯作者信息:

  • [Zhang, Yong]Beijing Univ Technol, Beijing Artificial Intelligence Inst, Fac Informat Technol, Beijing Key Lab Multimedia & Intelligent Software, Beijing 100124, Peoples R China;;

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来源 :

IEEE TRANSACTIONS ON BIG DATA

ISSN: 2332-7790

年份: 2024

期: 4

卷: 10

页码: 556-567

7 . 2 0 0

JCR@2022

被引次数:

WoS核心集被引频次: 4

SCOPUS被引频次: 8

ESI高被引论文在榜: 0 展开所有

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