Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (2): 67-79.doi: 10.11896/jsjkx.240100167

• Database & Big Data & Data Science • Previous Articles     Next Articles

Multivariate Time Series Forecasting Based on Temporal Dependency and Variable Interaction

WANG Huiqiang1, HUANG Feihu1, PENG Jian1, JIANG Yuan2, ZHANG Linghao3   

  1. 1 College of Computer Science,Sichuan University,Chengdu 610065,China
    2 PLA 78135 Troop,Chengdu 610031,China
    3 State Grid Sichuan Electric Power Research Institute,Chengdu 610072,China
  • Received:2024-01-23 Revised:2024-06-05 Online:2025-02-15 Published:2025-02-17
  • About author:WANG Huiqiang,born in 2000,postgraduate,is a student member of CCF(No.J8853G).His main research intere-sts include time series analysis and deep learning.
    PENG Jian,born in 1970,Ph.D,professor,Ph.D supervisor,is an outstanding member of CCF(No.22761D).His main research interests include big data and wireless sensor network.
  • Supported by:
    Key Research and Development Program of Sichuan Province,China (2023YFG0112,2022YFG0034),Intelligent Terminal Key Laboratory of Sichuan Province(SCITLAB-20001) and Post-doctoral Interdisciplinary Innovation Fund of Sichuan University(10822041A2137).

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Abstract: Multivariate time series forecasting has a wide range of applications,such as power forecasting,weather forecasting.Although the latest models have achieved relatively good results,they still face the following challenges:1)it is difficult to fully consider the correlation between different variables in multivariate time series to make more accurate predictions;2)modelling the correlation between different variables usually requires a huge time and space cost.Current methods are mainly classified into va-riable-independent methods and variable-mixed methods.The variable-independent methods predict each variable based on its own information without considering the correlation between different variables;the variable-mixed methods embed all the variables into a high-dimensional hidden space without modelling the correlation between the variables in a targeted way,and cannot adequately capture the correlation between the variables.To address these challenges,this paper proposes a multivariate time series forecasting method FIID based on temporal dependence and variable interaction,which adequately models the correlations among different variables while greatly reducing the time and space costs.Specifically,this paper proposes variable fold based on the fact that correlations between different variables are usually sparse,which greatly reduces the time and space cost of subsequent mo-delling of correlations between different variables.Then this paper proposes the temporal dependence module to capture the global correlations within variables by linear transformation from the frequency perspective.Further,this paper defines the correlation between different variables as the correlation between different time periods of all variables,based on which this paper proposes the variable interaction module,which first aggregates the local information of the variables,and then models the global correlation between all the local features on this basis.With these two modules,not only the correlations between variables are adequately modeled,but also the time and space costs are greatly reduced compared to existing methods.The model FIID is experimented on twelve real datasets,and the results show that it achieves the best performance and possesses higher efficiency.

Key words: Variable interaction, Temporal dependency, Linear complexity, Variable fold

CLC Number: 

  • TP183
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