Local prediction of network traffic measurements data based on relevance vector machine

Authors:
Qingfang Meng;Yuehui Chen;Qiang Zhang;Xinghai Yang
Affiliations:
School of Information Science and Engineering, University of Jinan, Jinan, China,Shandong Provincial Key laboratory of Network Based Intelligent Computing, Jinan, China;School of Information Science and Engineering, University of Jinan, Jinan, China,Shandong Provincial Key laboratory of Network Based Intelligent Computing, Jinan, China;Institute of Jinan Semiconductor Elements Experimentation, Jinan, China;School of Information Science and Engineering, University of Jinan, Jinan, China,Shandong Provincial Key laboratory of Network Based Intelligent Computing, Jinan, China
Venue:
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
Year:
2013

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Abstract

In the reconstructed phase space, based on the nonlinear time series local prediction method and the relevance vector machine model, the local relevance vector machine prediction method was proposed in this paper, which was applied to predict the small scale traffic measurements data. The experiment results show that the local relevance vector machine prediction method could effectively predict the small scale traffic measurements data, the prediction error mainly concentrated on the vicinity of zero, and the prediction accuracy of the local relevance vector machine regression model was superior to that of the feedforward neural network optimized by PSO.