CAJ | 학술논문

针对原油价格预测问题,提出一种基于EMD（经验模式分解）和SVMs（支持向量机）的非线性组合预测方法。该方法运用EMD技术将原油价格序列分解成若干个不同频率的分量,根据频率高低将各分量分组叠加得到3个新序列,分别代表市场波动价格、重大事件价格、趋势价格;针对此3个序列,构建不同SVMs模型分别进行预测,得到各序列预测值;用SVMs针对各序列预测值构建组合模型得到最终预测值。采用WTI和Brent原油现货价格数据验证本方法的有效性,结果表明,此方法与单一的SVMs模型和人工神经网络模型相比,具有较高的预测精度。
침대원유개격예측문제,제출일충기우EMD（경험모식분해）화SVMs（지지향량궤）적비선성조합예측방법。해방법운용EMD기술장원유개격서렬분해성약간개불동빈솔적분량,근거빈솔고저장각분량분조첩가득도3개신서렬,분별대표시장파동개격、중대사건개격、추세개격;침대차3개서렬,구건불동SVMs모형분별진행예측,득도각서렬예측치;용SVMs침대각서렬예측치구건조합모형득도최종예측치。채용WTI화Brent원유현화개격수거험증본방법적유효성,결과표명,차방법여단일적SVMs모형화인공신경망락모형상비,구유교고적예측정도。
In order to predict world crude oil price,an empirical mode decomposition（EMD） based support vector machine（SVM） ensemble learning paradigm is proposed.The original crude oil spot price series are first decomposed into a finite number of independent intrinsic mode functions（IMFs）,with different frequencies.Then the IMFs are composed into three sub-series,namely fluctuating process,big events and a trend,based on fine-to-coarse reconstruction rule.Then different SVM models are used to model and forecast the three sub-series respectively.Finally,the forecasts of the three sub-series are combined with another SVM model to formulate an ensemble forecast for the original crude oil price series.To validate the proposed ensemble learning paradigm,two main crude oil price series,West Texas Intermediate（WTI） crude oil spot price and Brent crude oil spot price are used.The empirical results demonstrate effectiveness and attractiveness of the proposed EMD-based SVM ensemble learning paradigm compared with single SVMs and artificial neural networks.