GARCH非线性时间序列模型的网络流量预测  

Application of non-linear time series model GARCH in network traffic forecas

作　　者：黄世忠[1] 刘渊[1] 

机构地区：[1]江南大学数字媒体学院,江苏无锡214122

出　　处：《计算机工程与应用》2014年第5期83-85,95,共4页Computer Engineering and Applications

基　　金：国家自然科学基金(No.61103223);江苏省自然科学基金(No.BK201103);江苏省"六大高峰人才"计划

摘　　要：网络流量预测在拥塞控制、网络管理与诊断、路由器设计等领域都具有重要意义。根据当今网络流量的特点,传统的ARMA模型在描述网络流量数据特性时有一定的局限性,从而影响网络流量预测的精度。针对这个问题,研究了使用广义自回归条件异方差模型(GARCH)对网络流量数据进行建模的方法,通过仿真实验表明,该模型可以较好地描述网络流量数据的异方差性,同时其预测精度较之传统的ARMA模型的预测精度也得到了大幅提升。Forecasting of network traffic plays a significant role in many domains such as congestion control, network management and diagnose, and router design. In accordance with modern network, traditional Auto Regressive Moving Average（ARMA）model fails to describe the characteristic of network traffic very well. Therefore, Generalized Auto Regres-sive Conditional Heteroskedasticity（GARCH）model is studied for network traffic. The simulation shows that GARCH model is well fitted on the real data of network traffic. Meanwhile, the accuracy of the forecast based on the model is much better than that of ARMA model.

关 键 词：网络流量预测 非线性 广义自回归条件异方差模型 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]