延迟时间和嵌入维数联合优化的网络流量预测

Network traffic prediction based on jointly optimization of embedding dimension and delay time

为了提高网络流量的预测精度,利用相空间重构的两个关键参数—延迟时间(τ)和嵌入维(m)间的相互联系,提出一种延迟时间和嵌入维数联合优化的网络流量预测模型。该模型以最小二乘支持向量机作为网络流量预测算法,根据网络流量预测结果优劣评价指选择最优τ和m值,建立单步、多步网络流量预测模型,并通过仿真实验对模型的性能进行分析。结果表明,模型可以准确选择出最优嵌入维数和延迟时间,显著提高了网络流量的预测精度,预测结果明显优于独立优化τ和m以及传统联合优化τ和m的网络流量预测模型。

In order to improve the prediction accuracy of network traffic, a network traffic prediction method is proposed based on jointly optimization embedding dimension（m）and delay time（τ）of phase space reconstruction according the relation between embedding dimension and delay time. Least squares support vector machine is used as the network traffic prediction algorithm and the optimalτand m is selected according to prediction results of the network traffic, the simulation analysis is carried out on network traffic data to test the performance of single step and multi-step prediction model. The results show that the proposed method can effectively select the optimalτand m, significantly improve the prediction accuracy of network traffic, the prediction results is significantly higher than reference methods of the network traffic.