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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 12
3568-3573

Multivariate time series forecasting with spatio-temporal graph convolutional network

Li Huai'ao1,2,3,4
Zhou Xiaofeng1,2,3
Fang Lingshen5
Li Shuai1,2,3
Liu Shurui1,2,3
1. Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China
2. Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
3. Institute for Robotics & Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
4. University of Chinese Academy of Sciences, Beijing 100049, China
5. Kunshan Intelligent Equipment Research Institute, Suzhou Jiangsu 215347, China
DOI: 10.19734/j.issn.1001-3695.2022.05.0235

Abstract

In order to expand the prediction range of spatio-temporal graph convolutional network and apply them to the multivariate time series prediction problems in the scenario of unknown correlation, this paper proposed a graph learning based spatiotemporal graph convolutional network(GLB-STGCN). The graph learning layer learned the graph adjacency matrix from the time series with the help of cosine similarity, then the graph convolution network captured the interaction between multi-variables, and finally the multi-kernel time convolution network captured the periodic characteristics of the time series for precise prediction. To verify the effectiveness of GLB-STGCN, this paper used 4 public datasets from astronomy, electricity, transportation and economy and 1 industrial production dataset for the prediction experiments. The results show that GLB-STGCN outperforms the comparison methods, especially on astronomical datasets, with prediction errors reduced by 6.02%, 8.01%, 6.72%, and 5.31%, respectively. The experimental results prove that GLB-STGCN has a wider application range and better prediction effect, especially for time series prediction problems with obvious natural cycles.

Foundation Support

辽宁省重点研发计划资助项目(2020JH2/10100039)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.05.0235
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 12
Section: Algorithm Research & Explore
Pages: 3568-3573
Serial Number: 1001-3695(2022)12-006-3568-06

Publish History

[2022-08-02] Accepted Paper
[2022-12-05] Printed Article

Cite This Article

李怀翱, 周晓锋, 房灵申, 等. 基于时空图卷积网络的多变量时间序列预测方法 [J]. 计算机应用研究, 2022, 39 (12): 3568-3573. (Li Huai'ao, Zhou Xiaofeng, Fang Lingshen, et al. Multivariate time series forecasting with spatio-temporal graph convolutional network [J]. Application Research of Computers, 2022, 39 (12): 3568-3573. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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