物理学报Issue(13)：48-57,10.DOI:10.7498/aps.63.130504
一种基于相关分析的局域最小二乘支持向量机小尺度网络流量预测算法*
A lo cal least square supp ort vector machine prediction algorithm of small scale network traffic based on correlation analysis
摘要
Abstract
Real-time monitoring and forecasting technology for network traffic has played an important role in network man-agement. Effective network traffic prediction could analyze and solve problems before overload occurs, which significantly improves network availability. In this paper, after the vulnerability of traditional nonlinear prediction method in fore-casting modeling is analyzed, the relevant local (RL) forecast which is based on correlation analysis and the parameter optimization method based on pattern search (PS) is introduced. Using the correlation analysis, the optimal training subset is chosen from time-and distance-correlated training samples. On this basis, the prediction model is established by LSSVM. Finally network traffic dataset collected from wired campus networks is studied for our experiments. And the results show that the relevant local LSSVM prediction method whose training set and parameters have been auto-matically optimized can effectively predict the small scale traffic measurement data, and RL-LSSVM traffic forecasting algorithm exhibits significantly good prediction accuracy for the data set compared with previous algorithm.关键词
网络流量预测/混沌时间序列预测/最小二乘支持向量机/局域预测Key words
network traffic prediction/chaos time series forecasting/least squares support vector machine/local prediction引用本文复制引用
唐舟进,彭涛,王文博..一种基于相关分析的局域最小二乘支持向量机小尺度网络流量预测算法*[J].物理学报,2014,(13):48-57,10.基金项目
国防科技预研项目(批准号:208010201)资助的课题.@@@@Project supported by the Chinese Defence Advance Research Program of Science and Technology, China (Grant No.208010201) （批准号:208010201）
