关键词: 长相关, 短相关, 网络流量预测, Haar小波, 数学分析

Abstract: Many researches indicate that the Internet traffic exhibits Long-Range Dependence (LRD) and Short-Range Dependence (SRD) features simultaneously. To capture these features of traffic flow, a prediction method was proposed based on W-FARIMA (Wavelet Fractal Autoregressive Integrated Moving Average) model. First, original data series were decomposed by Haar wavelet into a low frequency signal and several high frequency signals. Second, the low frequency signal was modeled and predicted by FARIMA model. Third, the high frequency signals were predicted based on the weighted first order local prediction method. Finally, the respective prediction series were synthesized to get the final prediction data. The experimental results and mathematical analysis confirm that the model performs well in the long-term network traffic prediction.

Key words: Long-Range Dependence (LRD), Short-Range Dependence (SRD), network traffic prediction, Haar wavelet, mathematical analysis