A prediction comparison between univariate and multivariate chaotic time series  

混沌时间序列单变量和多变量重构的预测比较(英文)

作　　者：王海燕[1] 朱梅[2] 

机构地区：[1]东南大学经济管理学院,南京210096 [2]东南大学数学系,南京210096

出　　处：《Journal of Southeast University(English Edition)》2003年第4期414-417,共4页东南大学学报（英文版）

摘　　要：The methods to determine time delays and embedding dimensions in the phase space delay reconstruction of multivariate chaotic time series are proposed. Three nonlinear prediction methods of multivariate chaotic time series including local mean prediction, local linear prediction and BP neural networks prediction are considered. The simulation results obtained by the Lorenz system show that no matter what nonlinear prediction method is used, the prediction error of multivariate chaotic time series is much smaller than the prediction error of univariate time series, even if half of the data of univariate time series are used in multivariate time series. The results also verify that methods to determine the time delays and the embedding dimensions are correct from the view of minimizing the prediction error.提出了多变量混沌时间序列相空间延迟重构中延迟时间间隔和嵌入维数的选取方法 ,给出了多变量混沌时间序列的局部平均预测法 ,局部线性预测法和BP神经网络预测法等 3种非线性预测方法 .通过Lorenz系统的仿真计算表明 ,无论用 3种非线性预测方法中的哪一种 ,多变量混沌时间序列要比单变量混沌时间序列的预测误差小得多 ,即使前者的数据长度只有后者的一半 ,前者的预测误差也要小很多 .

关 键 词：multivariate chaotic time series phase space reconstruction PREDICTION neural networks 

分 类 号：N941.4[自然科学总论—系统科学] O415.5[理学—理论物理]