Abstract
The energy markets, particularly oil and gas, have been significantly affected by the outbreak of the COVID-19 pandemic in terms of price and availability. In addition to the pandemic, the Russia-Ukraine war has contributed to concerns about the reduction in the oil supply. AI techniques are widely employed for prediction oil prices as an alternative to traditional techniques. In this paper, an AI-based optimization model called adaptive fox-inspired optimization (AFOX) model is presented, then recurrent neural network (RNN) is combined with AFOX to form a hybrid model called recurrent neural network with adaptive fox-inspired (RNN-AFOX) model. The proposed model is used to predict Crude Oil Prices. In the proposed model, AFOX is used to find the best hyper-parameters of the RNN and employed these hyper-parameters to build best RNN structure and use it to forecast the closing price of the oil market. The results show that the RNN-AFOX model achieved a high accuracy prediction with very small error and the coefficient of determination (R-squared) equal to 0.99 outperforming the RNN model in terms of accuracy prediction by about 24%, the FOX model by about 20% and the AFOX model by about 14%. Moreover, RNN-AFOX was evaluated under the impact of the COVID-19 pandemic and the Russia-Ukraine war. The results show the efficiency of RNN-AFOX in forecasting the closing prices of oil with high accuracy. In general, the proposed RNN-AFOX model overcomes other studied models in terms of Mean Absolute Percentage Error, Mean Absolute Error, Mean Square Error, Root Mean Square Error, coefficient of determination (R-squared) and consumption time.
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Conceptualization, HALR and RJ; Methodology, HALR and RJ; Software, HALR and RJ; Formal Analysis, HALR and RJ; Data Curation, HALR and RJ; Writing-Original Draft Preparation, HALR and RJ, Writing—review and editing, HALR and RJ. All authors have read and agreed to the published version of the manuscript.
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ALRahhal, H., Jamous, R. RNN-AFOX: adaptive FOX-inspired-based technique for automated tuning of recurrent neural network hyper-parameters. Artif Intell Rev 56 (Suppl 2), 1981–2011 (2023). https://doi.org/10.1007/s10462-023-10568-3
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DOI: https://doi.org/10.1007/s10462-023-10568-3