Network Traffic Forecasting via Fuzzy Spatial-Temporal Fusion Graph Neural Networks

Spatial-temporal network traffic prediction remains a formidable challenge due to the intricate spatial relationships and dynamic temporal patterns characteristic of individual nodes. Traditional regression methods fall short in handling such graph-structured data effectively. In recent developments, Graph Neural Networks (GNNs) have shown promise in modeling these complex spatial-temporal interactions. Despite their potential, existing GNN-based approaches exhibit notable limitations: (1) they typically rely on fixed spatial adjacency matrices, which overlook fuzzy latent temporal dependencies; and (2) they often process spatial and temporal information independently, resulting in a loss of joint fuzzy dependencies or restricting the model to either global or local patterns alone. To overcome these issues, we introduce the Fuzzy Spatial-Temporal Fusion Graph Neural Network (FSTFGNN) based on Fuzzy Rough Sets. Our approach constructs a dynamic, data-driven fuzzy spatial-temporal fusion matrix to capture underlying low-level spatial-temporal joint relationships. Additionally, a fuzzy global-local unified GNN layer is incorporated to simultaneously learn global-local spatialtemporal dependencies. Experimental results on two real-world datasets validate the efficacy of the proposed FSTFGNN framework.