Relevance of hybrid artificial intelligence for improving the forecasting accuracy of natural resource prices  

作　　者：Mei Li Rida Waheed Dervis Kirikkaleli Ghazala Aziz 

机构地区：[1]School of Management and Economics and Shenzhen Finance Institute,The Chinese University of Hong Kong,Shenzhen(CUHK-Shenzhen),China [2]Department of Finance and Economics,College of Business,University of Jeddah,Jeddah,Saudi Arabia [3]European University of Lefke,Faculty of Economic and Administrative Science,Department of Banking and Finance,Lefke,Turkey [4]Department of Business Administration,College of Administrative and Financial Sciences,Saudi Electronic University,Jeddah,Saudi Arabia

出　　处：《Geoscience Frontiers》2024年第3期431-446,共16页地学前缘（英文版）

基　　金：the Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this research work through the project number MoF-IFUJ-22-20745-X.

摘　　要：The prediction performance of traditional forecasting methods is low due to the high level of complexity in a series of energy prices.The present study attempts to compare the traditional regression,machine learning tools and hybrid models to conclude the outperforming model.The first step is to propose the effective denoising technique for Tadawul energy index,which has confirmed the superiority of CSD based denoising.However,we use the CSD-ARIMA,CSD-ANN,and CSD-RNN as hybrid models.As a result,CSD-RNN outperforms both other models in terms of MSE,MAPE,RMSE and Dstat.The findings are useful for policy makers,investors and portfolio managers to forecast the energy trends,and hedge the portfolio risk accordingly.

关 键 词：Hybrid artificial intelligence CSD denoising technique Forecasting Energy prices Saudi Arabia 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]