基于多尺度主成分分析的ARIMA原油价格预测方法  

A Multi-scale Principal Component Analysis-based ARIMA Method for Forecasting Crude Oil Prices

作　　者：袁力 YUAN Li(Department of Basic Education,Wuhu Institute of Technology,Wuhu,Anhui 241003,China)

机构地区：[1]芜湖职业技术学院基础部,安徽芜湖241003

出　　处：《西昌学院学报（自然科学版）》2021年第3期28-32,共5页Journal of Xichang University(Natural Science Edition)

基　　金：安徽省自然科学基金资助项目(KJ2020A0916);安徽省质量工程资助项目(2019mooc396);芜湖职业技术学院教学研究资助项目(WZ〔2020〕jy20)。

摘　　要：石油作为“工业芯片”,原油价格波动会对全球的经济与政治安全造成影响,准确地预测原油价格未来信息一直备受各方关注。提出基于多尺度主成分分析(MSPCA)的ARIMA原油价格预测方法,考虑原油期货价格与现货价格之间的相关性,采用原油期货价格和现货价格序列组成的二维数据作为原始数据,数据经过MSPCA后利用ARIMA进行预测。该方法利用了小波变换的多尺度分析能力,PCA的降维统计能力和ARIMA模型对非平稳时间序列的预测能力,实验证实该预测方法优于经典ARIMA方法和Holt′s指数平滑法,有效地提高原油价格预测精度。The fluctuation of crude oil prices has an impact on the global economic and political security.Accurate prediction of the future crude oil prices has been all parties′concern.This paper proposes a multi-scale principal component analysis(MSPCA)-based ARIMA method for crude oil price forecasting,which uses two-dimensional crude oil price series as data source,uses the ARIMA for forecasting after MSPCA and fully considers the correlation between crude oil futures price and spot price.This method uses the multi-scale analysis ability of wavelet transform,the dimension reduction statistical ability of PCA,and the prediction ability of ARIMA model for non-stationary time series.The experiment proves that the ARIMA method based on MSPCA is better than ARIMA method and Holt′s exponential smoothing method,and thus can effectively improve the accuracy of forecasting crude oil prices.

关 键 词：原油价格预测 多尺度主成分分析 ARIMA模型 期货与现货价格 互信息 

分 类 号：F416.22[经济管理—产业经济] F764.1