关键词: 网络流量预测, 线性, 广义自回归条件异方差模型

Abstract: 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 Regressive 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.

Key words: network traffic forecast, non-linear, Generalized Auto Regressive Conditional Heteroskedasticity（GARCH） model