Fourier Graph Convolution Network for Time Series Prediction

Lyuchao Liao, Zhiyuan Hu, Chih-Yu Hsu () and Jinya Su
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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

Mathematics, 2023, vol. 11, issue 7, 1-19

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.

Keywords: traffic flow prediction; periodicity; volatility; Fourier embedding; spatial-temporal ChebyNet; graph convolutional neural network (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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