小波分析和AR-LSSVM的网络流量预测

为了提高网络流量的预测精度,提出了一种基于小波分析和AR-LSSVM的网络流量组合预测模型。利用Mallat算法对非平稳的网络流量序列进行分解和重构,得到低频信息和高频信息;对具有平稳特性的高频信息用AR模型进行预测,而对体现非平稳的低频信息用LSSVM进行预测;再将各模型的预测结果进行叠加,从而得到原始序列的预测值。仿真结果表明组合预测模型不仅具有较高的预测精度,而且预测性能稳定。

Network traffic prediction based on wavelet analysis and AR-LSSVM

For improving the prediction accuracy of network traffic,a new combination prediction model is proposed based on wavelet analysis and AR-LSSVM.The network traffic series are decomposed and reconstructed using Mallat algorithm, a low frequency signal and several high frequency signals are gotten.The high frequency signals with stationary characters are predicted with Auto-Regression（AR） models, and the low frequency with non-stationary character is predicted with Least Square Support Vector Machines（LSSVM）.The final prediction result of the original traffic series is the superimposition of these respective prediction results.The simulation results show that the method has higher prediction accuracy and steady prediction performance.