小波变换与GARCH组合模型的网络流量预测

在一些网络环境当中，网络流量具有非线性、异方差性和波动集群现象，传统的小波变换与ARMA组合模型不能很好地描述网络流量的这些特性。因此，研究使用了小波变换与广义自回归条件异方差GARCH组合模型来预测网络流量。首先，使用小波变换原理将网络流量序列分解成高频部分和低频部分，在此基础上对各个子序列分别建立相应的GARCH模型并进行预测；然后，使用小波变换原理将各个子序列的预测结果进行重构，从而最终实现对原始网络流量的预测。通过仿真实验表明，该模型的预测精度较之传统的小波变换与ARMA组合模型的预测精度得到了大幅提升。

Application of GARCH model with wavelet transform in network traffic forecast

Network traffic has characteristics of nonlinearity, heteroskedasticity, and volatility clustering in some network environments. Traditional models such as Auto Regressive Moving Average(ARMA) model with wavelet transform fail to describe these characteristics very well. Therefore, Generalized Auto Regressive Conditional Heteroskedasticity(GARCH) model with wavelet transform is studied for network traffic forecast. With wavelet transform, a network traffic series is divided into low frequency part and high frequency part, which are applied for GARCH modeling and forecasting respectively. Then, the forecast results of the sub series are reconstructed so as to implement the forecast of the original network traffic. The simulation shows that the accuracy of the forecast of GARCH model with wavelet transform is much better than that of ARMA model with wavelet transform.