Computer Science ›› 2019, Vol. 46 ›› Issue (12): 108-113.doi: 10.11896/jsjkx.181102207

• Network & Communication • Previous Articles     Next Articles

Mobile Traffic Forecasting Model Based on Spatio-temporal Features

ZHANG Jie1, BAI Guang-wei1, SHA Xin-lei1, ZHAO Wen-tian1, SHEN Hang1,2   

  1. (College of Computer Science and Technology,Nanjing Tech University,Nanjing 211816,China)1;
    (National Engineering Research Center for Communication and Network Technology,Nanjing University ofPosts and Telecommunications,Nanjing 210003,China)2
  • Received:2018-11-28 Online:2019-12-15 Published:2019-12-17

Abstract: Research shows that historical traffic data can be used for the prediction of mobile network traffic,and traffic information in surrounding areas can improve the accuracy of traffic prediction.To this end,this paper proposed the traffic prediction model STFM for mobile network based on spatio-temporal features.STFM uses the historical mobile traffic of the target area and surrounding areas to predict the traffic of the target area.Firstly,3D convolutional neural network(3D CNN) is used to extract the spatial features of the mobile network traffic,then time convolutional network (TCN) is used to extract the temporal features of the mobile network traffic.Finally,fully connected layers establish a mapping relationship between the real traffic and extracted features and generate a predicted traffic value.Validation and analysis of experiments show that the STFM reduce the normalized root mean square error (NRMSE) by 28%,21.7% and 10%,compared to TCN,CNN and CNN-LSTM Consequently,STFM can effectively improve the accuracy of mobile network traffic prediction.

Key words: Convolution neural network, Fully connected layers, Mobile network, Spatio-temporal feature, Traffic prediction

CLC Number: 

  • TP393
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