Ahmed Fathalla1, Zakaria Alameer2, Mohamed Abbas3, Ahmed Ali4,5,*
Computer Systems Science and Engineering, Vol.46, No.1, pp. 929-950, 2023, DOI: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… More >
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