混沌理论和LSSVM相结合的网络流量预测

Network traffic forecasting based on chaotic theory and Least Squares Support Vector Machine

为提高网络流量的预测精度,提出一种基于混沌理论和最小二乘支持向量机相结合的网络流量预测方法。采用相空间重构对网络流量时间序列进行重构,恢复网络流量的演化轨迹,采用非线性预测能力强的最小二乘支持向量机对网络流量时间序列进行训练建模,采用混沌粒子群算法对最小二乘支持向量机参数进行优化,从而获得最优网络流量预测模型。用实际网络流量数据对该算法有效性进行验证,结果表明该方法能够很好刻画网络流量的变化趋势,提高了网络流量的预测精度,预测性能优于传统的预测方法。

In order to improve the prediction accuracy of network traffic, this paper proposes a network traffic forecasting method based on chaotic theory and Least Squares Support Vector Machine. Phase space reconstruction is used to reconstruct the network traffic time series and restore the network flow evolution path, and then the network traffic time series are modeled and trained by Least Squares Support Vector Machines which has good nonlinear forecasting ability, and the parameters of Least Squares Support Vector Machine are optimized by chaotic particle swarm algorithm to obtain the optimal network traffic forecasting model. The forecasting method is tested by the network traffic time series data. The results show that the method can well depict the network flow change trend and improves the forecasting accuracy of network traffic whose forecasting performance is superior to the traditional forecasting method.