DDGNet: A Dual-Stage Dynamic Spatio-Temporal Graph Network for PM2.5 Forecasting

As air pollution problems become increasingly serious, PM2.5 forecasting based on spatio-temporal observation data has received widespread attention. This forecasting task is full of challenges given the complicated producing factors and fickle transmission process of PM2.5. However, most existing forecasting methods only exploit the spatial dependency by graph networks with fixed adjacency matrices, ignoring the dynamic spatio-temporal correlation of PM2.5 concentrations. In this paper, we propose a dual-stage dynamic spatio-temporal graph network (DDGNet) to model dynamic correlations for PM2.5 prediction of different cities. Specifically, DDGNet consists of two major stages: (1) dynamic graph construction to identify potentially informative neighbors for each node (a city) in every forecasting period; (2) graph attention networks to dynamically determine linking weights for each vertex to its neighbors. We evaluate DDGNet on three real-world datasets and compare it with several baselines. The experimental results demonstrate that our method achieves the state-of-the-art performance.