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Local Prediction of Network Traffic Measurements Data Based on Relevance Vector Machine

  • Qingfang Meng
  • Yuehui Chen
  • Qiang Zhang
  • Xinghai Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7952)

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.

Keywords

small-time scale network traffic measurements data nonlinear time series local prediction method relevance vector machine 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Qingfang Meng
    • 1
    • 2
  • Yuehui Chen
    • 1
    • 2
  • Qiang Zhang
    • 3
  • Xinghai Yang
    • 1
    • 2
  1. 1.School of Information Science and EngineeringUniversity of JinanJinanChina
  2. 2.Shandong Provincial Key laboratory of Network Based Intelligent ComputingJinanChina
  3. 3.Institute of Jinan Semiconductor Elements ExperimentationJinanChina

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