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

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

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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.

Hybrid artificial intelligenceCSD denoising techniqueForecastingEnergy pricesSaudi Arabia

Mei Li、Rida Waheed、Dervis Kirikkaleli、Ghazala Aziz

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School of Management and Economics and Shenzhen Finance Institute,The Chinese University of Hong Kong,Shenzhen(CUHK-Shenzhen),China

Department of Finance and Economics,College of Business,University of Jeddah,Jeddah,Saudi Arabia

European University of Lefke,Faculty of Economic and Administrative Science,Department of Banking and Finance,Lefke,Turkey

Department of Business Administration,College of Administrative and Financial Sciences,Saudi Electronic University,Jeddah,Saudi Arabia

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Deputyship for Research & Innovation,Ministry of Education in Saudi Arabia

MoF-IF-UJ-22-20745-X

2024

10.1016/j.gsf.2023.101670

地学前缘(英文版)
中国地质大学（北京） 北京大学

地学前缘(英文版)

CSTPCD
影响因子：0.576
ISSN：1674-9871
年,卷(期)：2024.15(3)
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