AFSTGCN:Prediction for multivariate time series using an adaptive fused spatial-temporal graph convolutional network

Yuteng Xiao ¹Kaijian Xia ²Hongsheng Yin ³Yu-Dong Zhang ⁴Zhenjiang Qian ⁵Zhaoyang Liu ³Yuehan Liang ³Xiaodan Li³

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作者信息

1. School of Information and Control Engineering China University of Mining and Technology,Xuzhou,221116,China;Department of Informatics,University of Leicester Leicester LE17RH,UK
2. The Affiliated Changshu Hospital of Soochow University,Changshu,215500,China
3. School of Information and Control Engineering China University of Mining and Technology,Xuzhou,221116,China
4. Department of Informatics,University of Leicester Leicester LE17RH,UK
5. School of Computer Science and Engineering,Changshu Institute of Technology,Suzhou,215500,China
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Abstract

The prediction for Multivariate Time Series(MTS)explores the interrelationships among variables at historical moments,extracts their relevant characteristics,and is widely used in finance,weather,complex industries and other fields.Furthermore,it is important to construct a digital twin system.However,existing methods do not take full advantage of the potential properties of variables,which results in poor predicted accuracy.In this paper,we propose the Adaptive Fused Spatial-Temporal Graph Convolutional Network(AFSTGCN).First,to address the problem of the unknown spatial-temporal structure,we construct the Adaptive Fused Spatial-Temporal Graph(AFSTG)layer.Specifically,we fuse the spatial-temporal graph based on the interrelationship of spatial graphs.Simultaneously,we construct the adaptive adjacency matrix of the spatial-temporal graph using node embedding methods.Subsequently,to overcome the insufficient extraction of disordered correlation features,we construct the Adaptive Fused Spatial-Temporal Graph Convolutional(AFSTGC)module.The module forces the reordering of disordered temporal,spatial and spatial-temporal dependencies into rule-like data.AFSTGCN dynamically and synchronously acquires potential temporal,spatial and spatial-temporal correlations,thereby fully extracting rich hierarchical feature information to enhance the predicted accuracy.Experiments on different types of MTS datasets demonstrate that the model achieves state-of-the-art single-step and multi-step performance compared with eight other deep learning models.

Key words

Adaptive adjacency matrix/Digital twin/Graph convolutional network/Multivariate time series prediction/Spatial-temporal graph

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基金项目

China Scholarship Council and the CERNET Innovation Project(20170111)

出版年

2024

数字通信与网络(英文)

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参考文献量38

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