一种改进的原油价格组合预测优化策略研究  

An improved research on optimization strategy of crude oil price forecast combination

作　　者：周浩 张逸飞 王震 王珏[1,2] 汪寿阳 ZHOU Hao;ZHANG Yifei;WANG Zhen;WANG Jue;WANG Shouyang(Academy of Mathematics and Systems Science,Chinese Academy of Sciences,Beijing 100190,China;University of Chinese Academy of Sciences,Beijing 100190,China)

机构地区：[1]中国科学院数学与系统科学研究院,北京100190 [2]中国科学院大学,北京100190

出　　处：《系统工程理论与实践》2021年第10期2660-2668,共9页Systems Engineering-Theory & Practice

基　　金：国家自然科学基金(71771208,71271202,71988101);国家能源集团能源需求与终端能源消费结构预测模型研究项目。

摘　　要：由于原油的重要性以及原油价格序列的非线性和复杂性,原油价格预测备受关注.组合预测是一项利用多个子模型提供的有用信息产生综合预测从而提高预测准确性的技术.原油价格组合预测研究的主要问题是如何有效地构建具有多样性的子模型,并确定最优组合权重.本文提出一种改进的原油价格组合预测模型.首先,采用多种特征选择技术,包括过滤式,包装式和嵌入式三大类方法,筛选出影响原油价格波动的关键因素.然后,基于不同特征选择方法筛选出的不同特征子集,结合多元线性回归,人工神经网络和支持向量回归预测模型,构建混合预测模型.在此基础上,提出一种动态粒子群优化算法,该算法能够搜索最优组合权值,并捕捉其在时间维度上的动态变化.实验结果表明,本文提出的动态组合预测模型能较大程度地降低计算复杂度,提高原油价格预测性能.Crude oil price forecasting received much attention due to its importance and the non-linearity and complexity of crude oil price series.Using the useful information provided by sub-model to generate comprehensive prediction,forecast combination aims to improve the forecasting accuracy.It is significant how to efficiently generate many diverse sub-models and weighting vector.In this paper,we first introduced various feature selection techniques,including filter,wrapper and embedded methods to determine the key factors affecting crude oil prices.Then,individual models are constructed by incorporating feature selection methods with multiple linear regression,artificial neural network and support vector regression model.Finally,a dynamic particle swarm optimization algorithm is proposed.The algorithm can search for the optimal weighting vector and capture the dynamic changes of weighting series.Experimental results show that the proposed dynamic forecast combination model can reduce the computational complexity and improve the forecasting performance.

关 键 词：原油价格 特征选择 组合预测 动态粒子群优化算法 

分 类 号：F426.22[经济管理—产业经济]