CAJ | 학술논문

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。
침대혼돈시간서렬예측문제，해문제출일충허의특정제취산법，이제고기예측정도。수선，채용평이불변소파변환，제취출혼돈시간서렬적세절화광활자층신식。연후，장혼돈서렬선성여비선성특정종가화관계추엄도함수관계。최후，이용소파자층신식급출일충신영적허의특정표체，이용우예측실험。채용경전적Mackey-Glass방진수거，이급미국밀서서비하실측경류류량수거실험표명，해문방법제고료예측정도，종이야험증료해문제출적허의특정제취산법재혼돈시간서렬예측상적유효성。동시，야가위공제、수문、기상등영역적혼돈현상결책제공삼고。
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.