Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (11A): 316-320.doi: 10.11896/jsjkx.200100085

• Computer Network • Previous Articles     Next Articles

Real-time Network Traffic Prediction Model Based on EMD and Clustering

YAO Li-shuang, LIU Dan, PEI Zuo-fei, WANG Yun-feng   

  1. School of Communication and Information Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,China
  • Online:2020-11-15 Published:2020-11-17
  • About author:YAO Li-shuang,born in 1993,postgra-duate.Her main research interests include machine learning and network management.
  • Supported by:
    This work was supported by the Changjiang Scholars and Innovative Research Team Program in University (IRT_16R72).

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Abstract: Based on the multiple characteristics of complex network traffic,the traditional single model has poor prediction results.In order to improve the accuracy and real-time performance of traffic prediction,a network traffic prediction model based on EMD and clustering is proposed.First,the network traffic is decomposed into IMFs through EMD.IMFs are on different time scales and their frequencies are relatively single.Secondly,IMFs are clustered by an improved K-means clustering algorithm,and IMFs with similar complexity are gathered.Then the clustered IMFs are predicted using the ARMA model.Finally,the predicted values of each IMF are summed to obtain the predicted value of overall network traffic.Experimental results show that,compared with the EMD-ARMA model,the model not only reduces the training time,and its MSE and MAE reduce by 3.8% and 7.6% respectively,APT improves by 6 percentage.The model achieves higher prediction accuracy of network traffic and can be used for real-time traffic prediction.

Key words: ARMA, EMD, K-means clustering, Network traffic, Traffic prediction

CLC Number: 

  • TP393
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