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A Deep Learning Ensemble Method for Forecasting Daily Crude Oil Price Based on Snapshot Ensemble of Transformer Model

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Computer Systems Science & Engineering 2023年第7期46卷 929-950页

作者： Ahmed Fathalla zakaria alameer Mohamed Abbas Ahmed Ali Department of Mathematics Faculty of ScienceSuez Canal UniversityIsmailia41522Egypt Mining and Petroleum Department Faculty of EngineeringAl-Azhar UniversityQenaEgypt School of Computer Science and Technology Central South UniversityChangshaHunanChina College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Higher Future Institute for Specialized Technological Studies Cairo3044Egypt

The oil industries are an important part of a country’s *** crude oil’s price is influenced by a wide range of ***,how accurately can countries predict its behavior and what predictors to employ are two main *** this view,we propose utilizing deep learning and ensemble learning techniques to boost crude oil’s price forecasting *** suggested method is based on a deep learning snapshot ensemble method of the Transformer *** examine the superiority of the proposed model,this paper compares the proposed deep learning ensemble model against different machine learning and statistical models for daily Organization of the Petroleum Exporting Countries(OPEC)oil price *** results demonstrated the outperformance of the proposed method over statistical and machine learning *** precisely,the proposed snapshot ensemble of Transformer method achieved relative improvement in the forecasting performance compared to autoregressive integrated moving average ARIMA(1,1,1),ARIMA(0,1,1),autoregressive moving average(ARMA)(0,1),vector autoregression(VAR),random walk(RW),support vector machine(SVM),and random forests(RF)models by 99.94%,99.62%,99.87%,99.65%,7.55%,98.38%,and 99.35%,respectively,according to mean square error metric.

关键词： Deep learning ensemble learning transformer model crude oil price

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