关键词: 混沌, 关联度, 加权, 局域线性模型

Abstract: When the embedded dimension of reconstructive phase space increase,applying the traditional adding-weight local-region model,which the weight of neighbor phase points is generally determined by space distance to forecast the chaotic time series,is not so satisfied.In the paper,taking the incidence-degree impact on the dynamical behavior of forecast center point into account,a novel adding-weight local-region linear model for forecasting network traffic is created,with the weight of neighbor phase points defined by incidence-degree between neighbor phase points with forecast center point.The result of simulation shows the presented model can greatly improve precision of network traffic forecasting when the embedded dimension is high,compared with the traditional method.

Key words: chaos, incidence degree, adding-weight, local-region linear model