基于二次分解和集成学习的粮食期货价格预测研究  

Research on grain futures price forecasting based on secondary decomposition and ensemble learning

作　　者：唐振鹏 吴俊传 张婷婷 杜晓旭 陈凯杰 TANG Zhenpeng;WU Junchuan;ZHANG Tingting;DU Xiaoxu;CHEN Kaijie(College of Economics and Management,Fuzhou University,Fuzhou 350108,China)

机构地区：[1]福州大学经济与管理学院,福州350108

出　　处：《系统工程理论与实践》2021年第11期2837-2849,共13页Systems Engineering-Theory & Practice

基　　金：国家自然科学基金面上项目(71573042,71973028)。

摘　　要：本文基于二次分解和集成学习的思想,构建VMD-EEMD-DE-ELM-DE-ELM组合模型,选取CBOT交易所上市的大豆,小麦及水稻期货作为国际粮食期货的代表,预测其未来收益率走势.鉴于目前已有研究均直接忽略VMD分解后残差项所含纳的重要信息,本文引入二次分解思想首次对其残差项进行EEMD二次分解、集成预测,改善其预测精度,进而提高模型整体预测精度.同时,针对现有组合模型预测方法采用等权重重构分量预测结果的缺陷,本文借鉴集成学习的思想,引入DE-ELM元学习器优化预测重构权重,优化模型全局预测表现.实证结果发现:本文提出的混合模型相较参照组模型具有显著的预测优势.Based on the idea of secondary decomposition and ensemble learning,we build the VMD-EEMD-DE-ELM-DE-ELM model,select soybeans,wheat and rice futures listed on the CBOT exchange as representatives of international grain futures,and predict its future price trend.In view of the existing research that directly ignore the residual items after VMD decomposition,we introduce the idea of secondary decomposition to perform the EEMD decomposition and ensemble prediction of its residual items for the first time.This method can capture the rich information contained in the residual items,thereby helping to improve the model’s prediction effect on the original sequence.At the same time,because of the shortcomings of the existing model which use equal weights to reconstruct the prediction results of components,we draw on the idea of ensemble learning and introduces the DE-ELM meta-learner to optimize the reconstruction weights to obtain the best overall prediction results of the model.The empirical results show that the model proposed by us has a significant predictive advantage over the existing models.

关 键 词：粮食期货 预测 二次分解 集成学习 

分 类 号：F830.9[经济管理—金融学]