MVSTGN: A Multi-View Spatial-Temporal Graph Network for Cellular Traffic Prediction
Yao Yang, Bo Gu, Zhou Su, Mohsen Guizani
Abstract
Timely and accurate cellular traffic prediction is difficult to achieve due to the complex spatial-temporal characteristics of cellular traffic. The latest approaches mainly aim to model local spatial-temporal dependencies of cellular traffic based on deep learning techniques but lack the consideration of diverse global spatial-temporal correlations hidden in cellular traffic. To tackle this issue, we propose a novel multi-view spatial-temporal graph network (MVSTGN), which combines attention and convolution mechanisms into traffic pattern analysis, enabling the comprehensive excavation of spatial-temporal characteristics. Specifically, the MVSTGN realizes the above statement from three spatial-temporal views: 1) From a global spatial view, two spatial attention modules are proposed to capture the global spatial correlations between different regions at node and trend levels; 2) From a global temporal view, a temporal attention module is employed to capture and encode global temporal correlations between traffic at different times; 3) From a local spatial-temporal view, a dense convolution module is developed to further excavate the local spatial-temporal dependencies in cellular traffic. Consequently, a successful cellular traffic prediction strategy is constructed to fully explore the spatial-temporal characteristics from multiple views. The experimental results on a popular real-world cellular traffic dataset demonstrate that the MVSTGN achieves obvious improvements over baselines.