Position: Home > Articles > Periodic Traffic Prediction Algorithm of Markov Based on Normal Smooth Sequence
Journal of Qingdao Agricultural University(Natural Science)
2010,27
(4)
76-79
基于正态平稳的马尔可夫周期流量预测算法
作 者：
时鸿涛
单 位：
中国海洋大学信息科学与工程学院
关键词：
流量模型;流量预测;正态平稳序列;马尔可夫模型
摘 要：
为提高网络流量预测的准确度,提出一种新的马尔可夫流量预测算法。将实际网络流量序列分解为周期序列和正态平稳序列,对正态平稳序列建立马尔可夫模型并对其进行预测,结合预测值和周期序列得到实际网络流量的预测结果。实验结果表明,该算法具有较高准确度。
译 名：
Periodic Traffic Prediction Algorithm of Markov Based on Normal Smooth Sequence
作 者：
SHI Hong-tao1,2(1.School of Information Science and Technology,Ocean University of China,Qingdao 266100,China;2.Network Center,QAU)
关键词：
traffic model;traffic prediction;normal smooth sequence;Markov model
摘 要：
In order to improve the accuracy of network traffic prediction,a new traffic prediction algorithm of Markov model is proposed.The actual network traffic is decomposed into periodic sequences and normal smooth sequence,and a Markov model is established to predict the normal smooth sequence,then the prediction of the actual network traffic can be obtained by the synthesis of predictive results and periodic sequences.As shown in a set of experiments,the algorithm is of higher accuracy in comparision with the traditional prediction algorithms of ?short range dependence models.
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