Fourier Graph Convolution Network for Time Series Prediction

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...

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Bibliographic Details
Published in:Mathematics (Basel) 2023-04, Vol.11 (7), p.1649
Main Authors: Liao, Lyuchao, Hu, Zhiyuan, Hsu, Chih-Yu, Su, Jinya
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Convolution
Deep learning
Embedding
Food science
Fourier embedding
Fourier series
graph convolutional neural network
Intelligent transportation systems
Machine learning
Methods
Modules
Network management systems
Neural networks
Pattern recognition
Periodic functions
periodicity
spatial-temporal ChebyNet
Time series
Time-series analysis
Traffic flow
traffic flow prediction
Traffic models
Transportation networks
Trends
Volatility
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Summary: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.
ISSN:2227-7390
2227-7390
DOI:10.3390/math11071649