Network traffic prediction by a wavelet-based combined model
Sun Han-Lin(孙韩林)a)†, Jin Yue-Hui(金跃辉)a), Cui Yi-Dong(崔毅东)a)b),and Cheng Shi-Duan(程时端)a)
a State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China; b School of Information and Telecommunication Engineering, Beijing University of Posts and Telecommunications, Beijing
Abstract Network traffic prediction models can be grouped into two types, single models and combined ones. Combined models integrate several single models and thus can improve prediction accuracy. Based on wavelet transform, grey theory, and chaos theory, this paper proposes a novel combined model, wavelet--grey--chaos (WGC), for network traffic prediction. In the WGC model, we develop a time series decomposition method without the boundary problem by modifying the standard à trous algorithm, decompose the network traffic into two parts, the residual part and the burst part to alleviate the accumulated error problem, and employ the grey model GM(1,1) and chaos model to predict the residual part and the burst part respectively. Simulation results on real network traffic show that the WGC model does improve prediction accuracy.
Received: 23 February 2009
Revised: 20 March 2009
Accepted manuscript online:
Fund: Project supported by National Basic Research Program
of China (Grant Nos 2009CB320505 and 2009CB320504) and National
High Technology Research and Development Program of China (Grant Nos
2006AA01Z235, 2007AA01Z206 and 2009AA01Z210).
Cite this article:
Sun Han-Lin(孙韩林), Jin Yue-Hui(金跃辉), Cui Yi-Dong(崔毅东),and Cheng Shi-Duan(程时端) Network traffic prediction by a wavelet-based combined model 2009 Chin. Phys. B 18 4760
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