Long-Term Prediction on Graph Data With Causal Network Construction

Graph data encode unobservable complex spatiotemporal information of complex systems, which brings a great challenge for accurate and stable long-term prediction. Therefore, long-term prediction of such networked flow data has always been one of the bottlenecks of modern complex systems. In previous studies, most researchers only paid attention to short-term prediction of graph data. The accuracy of these models deteriorates rapidly when they are applied in long-term prediction. In this study, a causation-based spatiotemporal feature extraction method with a novel deep learning framework named pyramid spatiotemporal network (PSTN) is proposed for long-term networked flow prediction tasks, which can achieve stable long-term spatiotemporal feature extraction with the constructed causal network, and the multiple temporal extraction mechanism. PSTN has achieved prior performance in the tested PeMSD7(M) and PeMS-Bay datasets, where the long-term prediction accuracy of PSTN is highly better than other widely used baseline prediction models, which may provide some insight into spatiotemporal prediction researches.