A Multi-scale Ensemble Learning Model for Cellular Traffic Prediction

Changzheng Gao, Tao Feng, Huandong Wang, Depeng Jin, Junlan Feng, Xing Wang, Lin Zhu, Chao Deng

Abstract: With the widespread use of mobile devices in recent years, accurate prediction of base station traffic is vital for maintaining a good quality of mobile network services. In this paper, we propose an ensemble learning framework to predict the cellular traffic of base stations. Specifically, we introduce the Granger causality test to find the causal relationship in the base stations and model the spatial relationship between them simultaneously. We also employ a temporal convolutional network (TCN) to extract the sequential temporal features of base station traffic. Aiming at modelling the long-tail characteristics of the traffic distribution of base stations, we use the technique of redundant encoding to refine the prediction task to learn the base station traffic of different scales combined with ensemble learning. Extensive experimental results demonstrate that our method can predict base station traffic precisely and outperforms the best baseline by nearly 13% on average in terms of NMSE and NRMSE.