Network Traffic Based on GARCH-M Model and Extreme Value Theory

作     者：沈菲 王洪礼 史道济 李栋 

作者机构：School of SciencesTianjin University School of Mechanical Engineering Tianjin University School of Management Tianjin University 

出 版 物：《Transactions of Tianjin University》 (天津大学学报（英文版）)

年 卷 期：2005年第11卷第5期

页      面：386-390页

核心收录：

学科分类：08[工学] 082303[工学-交通运输规划与管理] 082302[工学-交通信息工程及控制] 0823[工学-交通运输工程] 

基　　金：Supported by University and College Doctoral Subject Special Scientific Research Fund( No. 20040056041) 

主　　题：network traffic GARCH-M extreme value theory generalized Pareto distribution 

摘      要：GARCH-M ( generalized autoregressive conditional heteroskedasticity in the mean) model is used to analyse the volatility clustering phenomenon in mobile communication network traffic. Normal distribution, t distribution and generalized Pareto distribution assumptions are adopted re- spectively to simulate the random component in the model. The demonstration of the quantile of network traffic series indicates that common GARCH-M model can partially deal with the fat tail problem. However, the fat tail characteristic of the random component directly affects the accura- cy of the calculation. Even t distribution is based on the assumption for all the data. On the other hand, extreme value theory, which only concentrates on the tail distribution, can provide more ac- curate result for high quantiles. The best result is obtained based on the generalized Pareto distribu- tion assumption for the random component in the GARCH-M model.