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

Ahmed Fathalla1, Zakaria Alameer2, Mohamed Abbas3, Ahmed Ali4,5,*

1 Department of Mathematics, Faculty of Science, Suez Canal University, Ismailia, 41522, Egypt
2 Mining and Petroleum Department, Faculty of Engineering, Al-Azhar University, Qena, Egypt
3 School of Computer Science and Technology, Central South University, Changsha, Hunan, China
4 College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia
5 Higher Future Institute for Specialized Technological Studies, Cairo, 3044, Egypt

* Corresponding Author: Ahmed Ali. Email: email

Computer Systems Science and Engineering 2023, 46(1), 929-950. https://doi.org/10.32604/csse.2023.035255

Abstract

The oil industries are an important part of a country’s economy. The crude oil’s price is influenced by a wide range of variables. Therefore, how accurately can countries predict its behavior and what predictors to employ are two main questions. In this view, we propose utilizing deep learning and ensemble learning techniques to boost crude oil’s price forecasting performance. The suggested method is based on a deep learning snapshot ensemble method of the Transformer model. To 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 forecasting. Experimental results demonstrated the outperformance of the proposed method over statistical and machine learning methods. More 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.

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APA Style
Fathalla, A., Alameer, Z., Abbas, M., Ali, A. (2023). A deep learning ensemble method for forecasting daily crude oil price based on snapshot ensemble of transformer model. Computer Systems Science and Engineering, 46(1), 929-950. https://doi.org/10.32604/csse.2023.035255
Vancouver Style
Fathalla A, Alameer Z, Abbas M, Ali A. A deep learning ensemble method for forecasting daily crude oil price based on snapshot ensemble of transformer model. Comput Syst Sci Eng. 2023;46(1):929-950 https://doi.org/10.32604/csse.2023.035255
IEEE Style
A. Fathalla, Z. Alameer, M. Abbas, and A. Ali "A Deep Learning Ensemble Method for Forecasting Daily Crude Oil Price Based on Snapshot Ensemble of Transformer Model," Comput. Syst. Sci. Eng., vol. 46, no. 1, pp. 929-950. 2023. https://doi.org/10.32604/csse.2023.035255



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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