Graph Attention Spatial-Temporal Network for Deep Learning Based Mobile Traffic Prediction

Abstract

With the rapid development of mobile cellular technologies and the popularity of mobile devices, timely mobile traffic forecasting with high accuracy becomes more and more critical for proactive network service provisioning and efficient network resource allocation. Due to the complicated dynamic nature of mobile traffic demand, traditional time series methods cannot satisfy the requirements of prediction tasks well and often neglect the important spatial factors. In addition, while some recent approaches model mobile traffic prediction problem using temporal and spatial features, they only consider local geographical dependency and do not take influential distant regions into consideration. In this paper, we propose Graph Attention Spatial-Temporal Network (GASTN), a novel deep learning framework to tackle the mobile traffic forecasting problem. Specifically, GASTN considers spatial correlation through the geographical relation graph and utilizes structural recurrent neural networks to model the global near-far spatial relationships as well as capture the temporal dependencies between future demand for mobile traffic and historical traffic volume. Besides, two attention mechanisms are proposed to integrate different effects in a holistic way. Extensive experiments on a large-scale real-world mobile traffic dataset demonstrate that our model significantly outperforms the state-of-the-art methods.