An Integrated Prediction Model for Network Traffic based on Wavelet Transformation

Authors

  • Lei Ding School of Physics Science and Information Engineering, Jishou University
  • Jinhua She School of Computer Science, Tokyo University of Technology
  • Sheng Peng School of Physics Science and Information Engineering, Jishou University

DOI:

https://doi.org/10.5755/j01.eee.19.3.3700

Keywords:

Wavelet transforms, prediction methods, communication system traffic, neural networks

Abstract

To deal with the characters with the changing trend of the steady state and dynamic state of network traffic, an integrated prediction model for network traffic based on wavelet transformation is presented in this paper. First, the network traffic is decomposed using wavelet transformation and is reconstructed using a single branch algorithm. Next, the low frequency components of the network traffic are predicted using an improved gray theory. They are used to depict the changing trend of the steady state. Then, the high frequency components of the network traffic are predicted using a BP neural network algorithm. They are used to reveal the dynamic effect. Finally, all those results are synthesized to predict the network traffic. Simulation results show that the presented integrated model increased the prediction accuracy and decreased the negative effect brought by the burstiness and uncertainty.

DOI: http://dx.doi.org/10.5755/j01.eee.19.3.3700

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Published

2013-03-07

How to Cite

Ding, L., She, J., & Peng, S. (2013). An Integrated Prediction Model for Network Traffic based on Wavelet Transformation. Elektronika Ir Elektrotechnika, 19(3), 73-76. https://doi.org/10.5755/j01.eee.19.3.3700

Issue

Vol. 19 No. 3 (2013)

Section

SYSTEM ENGINEERING, COMPUTER TECHNOLOGY

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