Predicting Future Spatio-Temporal States Using a Robust Causal Graph Attention Model

Spatiotemporal prediction is a research topic in urban planning and management. Most existing spatiotemporal prediction models currently face challenges. More specifically, most prediction models are sensitive to missing data, meaning most prediction models are only tested on spatiotemporal data assuming no missing data. Although missing data can be imputed, spatiotemporal prediction models with the capability of handling missing data are needed. In this study, we propose a novel missing-data-tolerant causal graph attention model called CGATM to address the above challenges. To enable the CGATM model to be tested on spatiotemporal data with missing data, we propose a novel missing data handling mechanism that automatically handles missing data according to the probability of data missing patterns. To improve the nonlinear fitting ability of the CGATM model, we propose a novel causal graph attention method that represents geospatial heterogeneity by adjacent nodes with different weights. In addition, we design the CGTAM model as an Imputer-Predictor architecture and define a novel loss function to optimize model parameters. The proposed model was validated on three real-world spatiotemporal datasets (traffic dataset, PM2.5 dataset, and temperature dataset). Experimental results showed that the proposed model has better prediction performance under four missing scenarios, and outperforms eight existing baselines regarding prediction accuracy.