Early Spatiotemporal Event Prediction via Adaptive Controller and Spatiotemporal Embedding

Given the increasing importance of predicting spatiotemporal events such as wildfire, crime, and traffic congestion, existing methods are faced with the challenge of balancing timeliness and accuracy. Late predictions may result in tremendous economic costs and human life loss, while inaccurate predictions are likely to cause unnecessary public resources and social anxiety. Therefore, balancing accuracy and timeliness is essential in general spatiotemporal event prediction problems. In this paper, we propose an Early Spatiotemporal Graph Convolutional Network (ESTGCN)
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to adaptively determine the optimal prediction time, which makes a tradeoff between prediction accuracy and timeliness and addresses two major questions: 1) How can we determine optimal prediction time points for different areas, taking into account their unique characteristics and conditions? 2) How can we minimize the propagation of prediction errors throughout the forecast timeline? Extensive experiments on two large-scale real-world datasets demonstrate that our proposed approaches can give an optimal prediction time in advance for each area and outperform all baselines in early spatiotemporal prediction tasks.