计算机工程与应用2012，Vol.48Issue(21)：103-106,4.DOI:10.3778/j.issn.1002-8331.2012.21.022
基于加权支持向量回归的网络流量预测
Network traffic forecast based on weighted support vector regression
摘要
Abstract
The forecast of network traffic is important to the security and availability of network. However, the traditional prediction methods based on uniform time weight lack generalizing ability, which results in the low prediction accuracy. The time weight of each history traffic data is calculated based on the time interval to the prediction point. The time weighted support vector regression model w-SVR is utilized to predict the network traffic. The prediction accuracy is improved attributing to the generalizing ability of w-SVR and the unique weight of each training data. The experimental results show that the prediction errors rate of w-SVR is decreased by 37.4% and 65.6% compared with ANN and AR model while its standard deviation is decreased by 46.2% and 53.3%.关键词
网络流量/预测/支持向量机/网络安全Key words
network traffic/ prediction/ Support Vector Machine (SVM)/ network security分类
信息技术与安全科学引用本文复制引用
赵云,肖嵬,陈阿林..基于加权支持向量回归的网络流量预测[J].计算机工程与应用,2012,48(21):103-106,4.基金项目
重庆师范大学青年教师基金项目(No.09XLQ12). （No.09XLQ12）
