When Spatio-Temporal Meet Wavelets: Disentangled Traffic Forecasting via Efficient Spectral Graph Attention Networks

Traffic forecasting is crucial for public safety and resource optimization, yet is very challenging due to the temporal changes and the dynamic spatial correlations of the traffic data. To capture these intricate dependencies, spatio-temporal networks, such as recurrent neural networks with graph convolution networks, graph convolution networks with temporal convolution networks, and temporal attention networks with full graph attention networks, are applied. However, previous spatiotemporal networks are based on end-to-end training and thus fail to handle the distribution shift in the non-stationary traffic time series. On the other hand, the efficient and effective algorithm for modeling spatial correlations is still lacking in prior networks. In this paper, rather than proposing yet another end-to-end model, we aim to provide a novel disentangle fusion framework STWave to mitigate the distribution shift issue. The framework first decouples the complex traffic data into stable trends and fluctuating events, followed by a dual-channel spatio-temporal network to model trends and events, respectively. Finally, reasonable future traffic can be predicted through the fusion of trends and events. Besides, we incorporate a novel query sampling strategy and graph wavelet-based graph positional encoding into the full graph attention network to efficiently and effectively model dynamic spatial correlations. Extensive experiments on six traffic datasets show the superiority of our approach, i.e., the higher forecasting accuracy with lower computational cost.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Traffic Prediction PeMS07 STWave MAE@1h 19.94 # 8
Traffic Prediction PeMS08 STWave MAE@1h 13.42 # 2
Traffic Prediction PeMSD3 STWave 12 steps MAE 14.93 # 3
Traffic Prediction PeMSD4 STWave 12 steps MAE 18.50 # 5
Traffic Prediction PeMSD8 STWave 12 steps MAE 13.42 # 1