LH-GSTGNN: Lag-Heterogeneity Guided Spatio-temporal Graph Neural Network

Spatio-temporal prediction is fundamental to a wide range of applications, including traffic flow forecasting and air quality monitoring. However, real-world spatio-temporal systems are rarely governed by homogeneous or synchronized interactions. Spatial dependencies often vary across regions, temporal patterns evolve at multiple scales, and the influence of one location on another may emerge with dynamic, region-specific delays rather than in a synchronized manner. These heterogeneous and asynchronous lag characteristics pose substantial challenges to accurate prediction, whereas most existing methods rely on static spatial graphs or synchronized temporal modeling, which limits their ability to capture complex real-world dynamics. To address this, we propose the Lag-Heterogeneity Guided Spatio-temporal Graph Neural Network (LH-GSTGNN). Rather than relying on stationary assumptions, LH-GSTGNN treats lag heterogeneity as an explicit modeling target. It characterizes evolving spatial dependencies, captures temporal dynamics across multiple ranges, and highlights delayed responses embedded in intermediate representations. In this way, the proposed framework preserves heterogeneous and asynchronous interactions that are otherwise prone to being smoothed out, yielding a more faithful representation of real-world spatio-temporal dynamics. Extensive experiments on nine real-world datasets covering traffic flow, traffic speed, and air quality prediction show that LH-GSTGNN consistently outperforms strong baselines, achieving up to 4.9% lower MAE and 2.9% lower RMSE than the second-best method. Visualization-based case studies further demonstrate its effectiveness in modeling both spatial heterogeneity and diverse lagged fluctuations.