小时间尺度网络流量混沌性分析及趋势预测

小时间尺度的网络流量的混沌性被噪声掩盖难以预测,本文通过局部投影降噪得到可预测的混沌性流量趋势.针对网络流量存在的时变性和长周期性,提出一种最优样本子集在线模糊最小二乘支持向量机(Least Squares Support Vector Machine,LSSVM)预测方法:以与预测样本时间上以及欧式距离最近的样本点构...

The Chaotic Analysis and Trend Prediction on Small-Time Scale Network Traffic

The chaotic performance of small-time scale network traffic was covered by noise,which made the traffic unpredictable.This paper introduces the local projection to denosie network traffic;a chaotic and predictable traffic trend is obtained.As the network traffic series is long-period and time-varying,a new method named optimal training subset online fuzzy least squares support vector machines(OTSOF-LSSVM) is proposed.Samples temporal and distance nearest to prediction sample are chosen as optimal training subset,and the subset are fuzzified.On this basis,the prediction model is established by fuzzy LSSVM.The model update computational complexity is reduced by partitioned matrix calculation.The noise reduction and trend prediction on network traffic shows the proposed method can predict the trend quickly and exactly.