Adaptive graph convolutional recurrent neural networks for system-level mobile traffic forecasting

Accurate traffic pattern prediction in large-scale networks is of great importance for intelligent system management and automatic resource allocation. System-level mobile traffic forecasting has significant challenges due to the tremendous temporal and spatial dynamics introduced by diverse Internet user behaviors and frequent traffic migration. Spatial-temporal graph modeling is an efficient approach for analyzing the spatial relations and temporal trends of mobile traffic in a large system. Previous research may not reflect the optimal dependency by ignoring inter-base station dependency or pre-determining the explicit geological distance as the interrelationship of base stations. To overcome the limitations of graph structure, this study proposes an adaptive graph convolutional network (AGCN) that captures the latent spatial dependency by developing self-adaptive dependency matrices and acquires temporal dependency using recurrent neural networks. Evaluated on two mobile network datasets, the experimental results demonstrate that this method outperforms other baselines and reduces the mean absolute error by 3.7 % and 5.6 % compared to time-series based approaches.