计算机科学 ›› 2020, Vol. 47 ›› Issue (11A): 316-320.doi: 10.11896/jsjkx.200100085
姚立霜, 刘丹, 裴作飞, 王云锋
YAO Li-shuang, LIU Dan, PEI Zuo-fei, WANG Yun-feng
摘要： 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。
中图分类号:
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