Fourier Graph Convolution Network for Time Series Prediction

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