基于ACO优化的ARIMA-ES-RF布伦特原油价格预测研究

doi:10.3969/j.issn.1671-1807.2024.05.015

	基于ACO优化的ARIMA-ES-RF布伦特原油价格预测研究
其他题名	Price Prediction of ARIMA-ES-RF Brent Crude Oil Based on ACO Optimization
	黄玲; 任苏灵
	2024-03-15
发表期刊	科技和产业
卷号	24 期号:5 页码:111-119
摘要	为解决传统单一的自回归积分滑动平均(ARIMA)和指数平滑(ES)预测原油价格(以布伦特原油为例)误差较大,难以精确地预测序列非线性特征的问题,提出自回归积分滑动平均(ARIMA)-指数平滑(ES)-随机森林(RF)组合预测方法.目前虽已有大量的原油价格预测模型,但还未有文献利用随机森林组合传统时间序列模型对原油价格进行研究.在此基础上,利用蚁群算法(ACO)对随机森林的重要参数(树的数量及根的深度)进行智能搜索,将随机森林组合模型的预测精度反馈给蚁群进行信息素的实时更新,输出使得组合模型预测精度最高的模型参数.研究结果表明:提出的蚁群优化参数后的组合随机森林模型能更好地预测布伦特原油价格的趋势,预测精度均方根误差(RMSE)从1.15降低至0.88,减少了 0.27;平均相对误差从1.10％降低至0.86％,降低了 0.24％,预测精度较以往的原油价格预测模型有显著提升.
其他摘要	To address the issue of significant errors in forecasting Brent crude oil prices using conventional single-method approaches like autoregressive integrated moving average(ARIMA)and exponential smoothing(ES),which struggle to precisely capture the nonlinear characteristics of the series,a combined forecasting method is proposed:autoregressive integrated moving average(ARIMA)-exponential smoothing(ES)-random forest(RF).While numerous models for predicting oil prices exist,no literature has yet explored the use of random forest in combination with traditional time series models for oil price forecasting.Building upon this,ant-colony optimization(ACO)is utilized to intelligently search for critical parameters(number of trees and depth of roots)within the Random Forest.The predictive accuracy of the combined model with Random Forest is fed back to the ant colony for real-time updates of information pheromones,yielding optimal model parameters that maximize predictive accuracy.The research findings demonstrate that the combined random forest model,optimized with ant colony parameters,yields improved trend prediction for Brent crude oil prices.The root mean square error(RMSE)drops from 1.15 to 0.88,marking a reduction of 0.27.Additionally,the mean absolute percentage error(MAPE)decreases from 1.10％to 0.86％,a reduction of 0.24％.This enhancement in predictive accuracy significantly surpasses previous models used for oil price forecasting.
关键词	随机森林蚁群算法布伦特原油价格
DOI	10.3969/j.issn.1671-1807.2024.05.015
URL	查看原文
ISSN	1671-1807
语种	中文
原始文献类型	Periodical
文献类型	期刊论文
条目标识符	http://ir.lzufe.edu.cn/handle/39EH0E1M/37471
专题	统计与数据科学学院
作者单位	兰州财经大学统计与数据科学学院,兰州 730000
第一作者单位	统计与数据科学学院
推荐引用方式 GB/T 7714	黄玲,任苏灵. 基于ACO优化的ARIMA-ES-RF布伦特原油价格预测研究[J]. 科技和产业,2024,24(5):111-119.
APA	黄玲,&任苏灵.(2024).基于ACO优化的ARIMA-ES-RF布伦特原油价格预测研究.科技和产业,24(5),111-119.
MLA	黄玲,et al."基于ACO优化的ARIMA-ES-RF布伦特原油价格预测研究".科技和产业 24.5(2024):111-119.