基于组合模型的自相似业务流量预测

针对经验模式分解存在的模态混叠问题,提出了一种基于组合模型的自相似业务流量预测方法。首先通过对网络流量进行集合经验模式分解,有效地去除自相似网络流量中存在的长相关性。接着根据分解得到的各本征模态函数分量的不同特性,分别采用人工神经网络与自回归滑动平均模型对其进行预测,最终再将预测结果进行组合。仿真结果表明,提出的方法对于实际网络流量数据具有较高的预测精度。

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.