Keywords: 
• 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

Abstract: Real-time monitoring and forecasting technology for network tra?c has played an important role in network man-agement. Effective network tra?c 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 tra?c 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 tra?c measurement data, and RL-LSSVM tra?c forecasting algorithm exhibits significantly good prediction accuracy for the data set compared with previous algorithm.