Leveraging Heterophily in Spatial-Temporal Graphs for Multivariate Time-Series Forecasting

Multivariate Time-Series (MTS) forecasting is challenging due to the complex spatial-temporal dependencies inherent in MTS data. Recent studies typically adopt spatial-temporal graph models to leverage this information. However, most of these approaches assume homophily in graphs and perform only immediate message passing, which limits their ability to fully capture spatial-temporal dependencies. In this paper, we propose a novel Dual-Path Spatial-Temporal Graph Neural Network (DPSTGNN). We first extract and cluster representative short-term series patterns from the MTS data. Then, by comparing historical series segments with the extracted representative patterns, we generate pattern attribution embeddings for nodes, enabling the construction of Homophily and Heterophily Spatial-Temporal Crossed Graphs (STCGs), which depict diverse node-wise relationships across different time segments. Finally, we introduce dilated spatial-temporal crossed graph convolutions on both types of STCGs to capture a wide range of spatial-temporal dependencies for enhanced MTS forecasting. Experiments on six real-world datasets demonstrate that our method outperforms state-of-the-art models.