Combination Model-based Self-similarity Traffic Prediction

In view of mode mixing caused by EMD(Empirical Mode Decomposition),this paper proposed a self-similarity traffic prediction method based on the combination models.Through the EEMD(Ensemble Empirical Mode Decomposition) process,the long-term dependence existing in network traffic was removed effectively.Additionally,according to the different characteristics of each IMF(Intrinsic Mode Function) produced by EEMD,ANN(Artificial Neural Network) and ARMA(Auto Regressive Moving Average) were adopted for different IMFs.The simulation results demonstrate that the proposed method can effectively predict the traffic and has high precision.