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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 \grave\rm a 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. 相似文献
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