摘要: 采用熵权系数代替空间距离来确定相空间邻近相点及其权重的方法,提出了一种网络流量预测的加权局域线性模型,该模型克服了用距离来确定相空间邻近相点及其权重的传统局域模型在预测高嵌入维的混沌时间序列时预测精度下降的缺点。模拟试验结果表明,和传统加权模型相比,当网络流量时间序列的嵌入维数较高时,该模型能大幅度提高预测精度。
关键词:
网络流量,
熵权,
加权,
局域线性模型
Abstract: Applying entropy coefficient instead of space distance to determine the neighbor point with their weights in phase space, a novel weighted local linear model is created, this model can overcome the defect of the traditional weighted local model in which the neighbor points with their weights are generally determined by distance so that when it is used to forecast the chaotic time series with the high embedded dimension are not so effective. The result of simulation shows the presented model can greatly improve precision of network traffic forecast when the embedded dimension of network traffic time series is high, compared with the traditional method.
Key words:
network traffic,
entropy coefficient,
weighted,
local linear model
中图分类号:
雷 霆;余镇危. 基于熵权的网络流量预测加权局域线性模型[J]. 计算机工程, 2007, 33(22): 113-115,.
LEI Ting; YU Zheng-wei. Weighted Local Linear Model of Network Traffic Forecast Based on Entropy Coefficient[J]. Computer Engineering, 2007, 33(22): 113-115,.