一种面向混沌时间序列预测的虚拟特征提取算法

摘要

This research focuses on the issue of chaotic time series prediction. A virtual feature extraction method for forecasting performance improvement is proposed. Firstly, details and smooths of the chaotic time series are extracted by shift invariant wavelet algorithms. Then the relationship between the linear and nonlinear components is extrapolated from additive to functional one. Finally, a novel virtual feature expression is given based on the above wavelet details and smooths for forecasting. Experiment results of forecasting on Mackey-Glass and real Mississippi River flow series show that the proposed method is superior over some existing methods, which also demonstrate the effectiveness of the proposed virtual feature extraction method. Moreover, the results may provide a decision-making reference for a variety of chaotic areas, such as control, hydrology, and meteorology.%针对混沌时间序列预测问题，该文提出一种虚拟特征提取算法，以提高其预测精度。首先，采用平移不变小波变换，提取出混沌时间序列的细节和光滑子层信息。然后，将混沌序列线性与非线性特征从加和关系推广到函数关系。最后，利用小波子层信息给出一种新颖的虚拟特征表达，以用于预测实验。采用经典的Mackey-Glass仿真数据，以及美国密西西比河实测径流流量数据实验表明，该文方法提高了预测精度，从而也验证了该文提出的虚拟特征提取算法在混沌时间序列预测上的有效性。同时，也可为控制、水文、气象等领域的混沌现象决策提供参考。