CAJ | 학술논문

针对具有非线性、非平稳、多尺度特性的复杂时间序列，提出一种基于集合经验模态分解（EEMD）和进化核主成分回归（KPCR）的自适应预测建模方法．首先运用能克服传统EMD算法中模态混叠现象的EEMD算法，按原始时间序列信号的构成特点将其分解到不同尺度，然后对不同尺度序列采用C—C方法重构相空间，在相空间中运用基于混合核函数的KPCR方法构建预测函数．同时，针对不同尺度序列预测模型的优选问题，采用粒子群优化（PSO）算法在给定准则下自适应确定各项参数，最后将不同尺度预测结果集成，得到实际时间序列的预测值．通过对国际原油价格的数据进行实证预测分析，表明了该方法能够在不同尺度对时间序列的变化趋势进行有效描述，自适应获取优化的预测模型．与现有方法相比，具有较强的自适应建模能力和较高的预测精度．
침대구유비선성、비평은、다척도특성적복잡시간서렬，제출일충기우집합경험모태분해（EEMD）화진화핵주성분회귀（KPCR）적자괄응예측건모방법．수선운용능극복전통EMD산법중모태혼첩현상적EEMD산법，안원시시간서렬신호적구성특점장기분해도불동척도，연후대불동척도서렬채용C—C방법중구상공간，재상공간중운용기우혼합핵함수적KPCR방법구건예측함수．동시，침대불동척도서렬예측모형적우선문제，채용입자군우화（PSO）산법재급정준칙하자괄응학정각항삼수，최후장불동척도예측결과집성，득도실제시간서렬적예측치．통과대국제원유개격적수거진행실증예측분석，표명료해방법능구재불동척도대시간서렬적변화추세진행유효묘술，자괄응획취우화적예측모형．여현유방법상비，구유교강적자괄응건모능력화교고적예측정도．
Aiming to some nonlinear, non-stationary, multi-scale characteristics of time series, an adaptive prediction modeling method based on ensemble empirical mode decomposition （EEMD） and evolution kernel principal component regression （KPCR） was proposed. Firstly, the original time series was decomposed into different scales by EEMD according to its composition characteristics, and then C-C method was applied to make the phase space reconstruction in every scale, where KPCR with a composite ker- nel was used to build a prediction function; at the same time, KPCR model was optimized with a given criteria by particle swarm optimization （PSO） algorithm in every scale, and finally the prediction results in different scales were integrated into the predicted value of time series. The results of the empirical prediction analysis for the international crude oil price show that this method can effectively describe the trend of time series in different scales and adaptively obtain the optimal prediction model, compared with the existing method, which has strong adaptive modeling capabilities and higher prediction accuracy.