首页|AFSTGCN:Prediction for multivariate time series using an adaptive fused spatial-temporal graph convolutional network
AFSTGCN:Prediction for multivariate time series using an adaptive fused spatial-temporal graph convolutional network
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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.
Adaptive adjacency matrixDigital twinGraph convolutional networkMultivariate time series predictionSpatial-temporal graph
Yuteng Xiao、Kaijian Xia、Hongsheng Yin、Yu-Dong Zhang、Zhenjiang Qian、Zhaoyang Liu、Yuehan Liang、Xiaodan Li
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School of Information and Control Engineering China University of Mining and Technology,Xuzhou,221116,China
Department of Informatics,University of Leicester Leicester LE17RH,UK
The Affiliated Changshu Hospital of Soochow University,Changshu,215500,China
School of Computer Science and Engineering,Changshu Institute of Technology,Suzhou,215500,China
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China Scholarship Council and the CERNET Innovation Project
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NSTL
万方数据
