基于EEMD和进化KPCR的复杂时间序列自适应预测建模

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.

EEMD and evolutionary KPCR based adaptive prediction modeling on complex time series

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 kernel 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.