Graph Structure Neural Differential Equations on Spatio-temporal Prediction

Deep learning is one of the most widely used modeling approach for spatio-temporal prediction. Traditional deep learning models get discrete features or hidden states from discrete data. However such discretization method cannot work well if the data is irregularly sampled or incompletely observed. It is difficult to obtain continuous features and hidden states for deep learning models. In this paper, we demonstrate how to use neural differential equations to solve this problem. We first validated the ability of Neural Controlled Differential Equations (Neural CDE) to process real-world irregular spatiotemporal data. Neural CDE is a type of neural differential equation that can obtain continuous hidden states. To make Neural CDE can obtain the spatial correlation of spatio-temporal data, we combined Graph Attention Network (GAT) with Neural CDE to propose a new model for spatio-temporal prediction. Furthermore, we used neural differential equations to designed a module to reduce the error of the prediction results. Experimental results show our proposed model outperforms the standard baseline by up to 37.40%.