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Fourier Graph Convolution Network for Time Series Prediction

Author

Listed:
  • Lyuchao Liao

    (Fujian Provincial Universities Engineering Research Center for Intelligent Driving Technology, School of Transportation, Fujian University of Technology, Fuzhou 350118, China)

  • Zhiyuan Hu

    (Fujian Provincial Universities Engineering Research Center for Intelligent Driving Technology, School of Transportation, Fujian University of Technology, Fuzhou 350118, China)

  • Chih-Yu Hsu

    (Fujian Provincial Universities Engineering Research Center for Intelligent Driving Technology, School of Transportation, Fujian University of Technology, Fuzhou 350118, China)

  • Jinya Su

    (Department of Computing Science, University of Aberdeen, Aberdeen AB24 3UE, UK)

Abstract

The spatio-temporal pattern recognition of time series data is critical to developing intelligent transportation systems. Traffic flow data are time series that exhibit patterns of periodicity and volatility. A novel robust Fourier Graph Convolution Network model is proposed to learn these patterns effectively. The model includes a Fourier Embedding module and a stackable Spatial-Temporal ChebyNet layer. The development of the Fourier Embedding module is based on the analysis of Fourier series theory and can capture periodicity features. The Spatial-Temporal ChebyNet layer is designed to model traffic flow’s volatility features for improving the system’s robustness. The Fourier Embedding module represents a periodic function with a Fourier series that can find the optimal coefficient and optimal frequency parameters. The Spatial-Temporal ChebyNet layer consists of a Fine-grained Volatility Module and a Temporal Volatility Module. Experiments in terms of prediction accuracy using two open datasets show the proposed model outperforms the state-of-the-art methods significantly.

Suggested Citation

  • Lyuchao Liao & Zhiyuan Hu & Chih-Yu Hsu & Jinya Su, 2023. "Fourier Graph Convolution Network for Time Series Prediction," Mathematics, MDPI, vol. 11(7), pages 1-19, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:7:p:1649-:d:1110772
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    References listed on IDEAS

    as
    1. Xinqiang Chen & Jinquan Lu & Jiansen Zhao & Zhijian Qu & Yongsheng Yang & Jiangfeng Xian, 2020. "Traffic Flow Prediction at Varied Time Scales via Ensemble Empirical Mode Decomposition and Artificial Neural Network," Sustainability, MDPI, vol. 12(9), pages 1-17, May.
    2. Robert Engle, 2004. "Risk and Volatility: Econometric Models and Financial Practice," American Economic Review, American Economic Association, vol. 94(3), pages 405-420, June.
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