ST-Tran: Spatial-Temporal Transformer for Cellular Traffic Prediction

Abstract

Accurate cellular traffic prediction is conducive to managing communication networks, but challenging, due to dynamic temporal variations and complicated spatial correlations. In this letter, a novel Spatial-Temporal Transformer (ST-Tran) is proposed to explore spatial and temporal sequence information simultaneously. A temporal transformer block is designed to learn temporal features of every grid in a communication network by modeling its traffic flows during both recent and periodic time intervals. Meanwhile, the spatial characteristics of every grid are cooperated with the information of its related grids to generate spatial predictions in the spatial transformer block. An output block is further proposed to merge the temporal and spatial information into a final prediction. Experimental results on a large real-world dataset verify the effectiveness of the ST-Tran. The source code is available at https://github.com/liuqingyao11/ST-Tran .