基于EMD聚类的实时网络流量预测模型

Real-time Network Traffic Prediction Model Based on EMD and Clustering

针对复杂的网络流量呈现出的多种特性,传统的单一模型预测效果差。为了提高流量预测的准确性和实时性,提出了一种基于经验模态分解(EMD)和聚类的网络流量预测模型。首先通过EMD将网络流量分解为不同时间尺度上频率单一的本征模函数(IMFs);其次通过改进的K均值聚类算法对IMF分量做聚类分析,将复杂度相近的IMF分量聚到一起;然后对聚类的IMF分量用自回归移动平均(ARMA)模型进行预测;最后将各IMF分量序列的预测值进行求和得到网络流量的预测值。实验结果证明,与EMD-ARMA模型相比,该模型不仅缩短了训练耗时,且均方误差(MSE)、平均绝对误差(MAE)分别下降了13.8%和7.6%,趋势预测准确率(APT)提高了6%,提高了网络流量的预测精度,可用于实时流量预测。

Based on the multiple characteristics of complex network traffic,the traditional single model has poor prediction results.In order to improve the accuracy and real-time performance of traffic prediction,a network traffic prediction model based on EMD and clustering is proposed.First,the network traffic is decomposed into IMFs through EMD.IMFs are on different time scales and their frequencies are relatively single.Secondly,IMFs are clustered by an improved K-means clustering algorithm,and IMFs with similar complexity are gathered.Then the clustered IMFs are predicted using the ARMA model.Finally,the predicted values of each IMF are summed to obtain the predicted value of overall network traffic.Experimental results show that,compared with the EMD-ARMA model,the model not only reduces the training time,and its MSE and MAE reduce by 3.8%and 7.6%respectively,APT improves by 6 percentage.The model achieves higher prediction accuracy of network traffic and can be used for real-time traffic prediction.