Network traffic prediction by a wavelet-based combined model

Network traffic prediction models can be grouped into twotypes, single models and combined ones. Combined models integrateseveral single models and thus can improve prediction accuracy.Based on wavelet transform, grey theory, and chaos theory, thispaper proposes a novel combined model, wavelet--grey--chaos (WGC),for network traffic prediction. In the WGC model, we develop a timeseries decomposition method without the boundary problem by modifyingthe standard graverm a trous algorithm, decompose the networktraffic into two parts, the residual part and the burst part toalleviate the accumulated error problem, and employ the grey modelGM(1,1) and chaos model to predict the residual part and theburst part respectively. Simulation results on real networktraffic show that the WGC model does improve prediction accuracy.