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<p>Given the volatile nature of oil prices in the wake of COVID-19 and the Russia-Ukraine war, the need for advanced prediction models is evident. The Autoregressive Integrated Moving Average model estimated through the maximum likelihood method with Marquardt-BFGS optimisation (ARIMA-BFGS) was used to select the relevant predictors for three different models: the Extreme Learning Machine (ELM), the newly introduced Evidential Neural Network for Regression with Gaussian Random Fuzzy numbers (EVNN-FUZZY) and an Artificial Neural Network fine-tuned with Particle Swarm Optimisation (ANN-PSO). Formal unit root tests, Augmented Dickey Fuller (ADF) and Phillips-Perron (PP) are used to test the stationarity of the Brent oil price before estimating ARIMA-BFGS. Evaluation measures such as root-mean-squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and coefficient of determination (<InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="521_2025_11306_Article_IEq1.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="21" /> </InlineMediaObject> <EquationSource Format="TEX">\(R^2\)</EquationSource> <EquationSource Format="MATHML"><math> <msup> <mi>R</mi> <mn>2</mn> </msup> </math></EquationSource> </InlineEquation>) are used to assess the performance of the models. The study utilises a combination of traditional methods and neural networks to improve the accuracy of the Brent oil price prediction. ANN-PSO improves the predictive precision of ARIMA-BFGS by 65.30% for the training dataset and 88.72% for the testing sample. The incorporation of COVID-19 and the Russia-Ukraine war has improved the performance of EVNN-FUZZY. Governments, investors and producers can all benefit from these outcomes while making financial decisions. The findings of this study can be used by oil-exporting economies to guide their budgets, while oil-importing countries can use them to manage inflation. </p>

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Gaussian random fuzzy and nature-inspired neural networks: a novel approach to Brent oil price prediction

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月29日

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  • Sagiru Mati,
  • Goran Yousif Ismael,
  • Abduallahi Garba Usman,
  • Ahmed Samour,
  • Nazifi Aliyu,
  • Raad Abdelhalim Ibrahim Alsakarneh,
  • Sani I. Abba

摘要

Given the volatile nature of oil prices in the wake of COVID-19 and the Russia-Ukraine war, the need for advanced prediction models is evident. The Autoregressive Integrated Moving Average model estimated through the maximum likelihood method with Marquardt-BFGS optimisation (ARIMA-BFGS) was used to select the relevant predictors for three different models: the Extreme Learning Machine (ELM), the newly introduced Evidential Neural Network for Regression with Gaussian Random Fuzzy numbers (EVNN-FUZZY) and an Artificial Neural Network fine-tuned with Particle Swarm Optimisation (ANN-PSO). Formal unit root tests, Augmented Dickey Fuller (ADF) and Phillips-Perron (PP) are used to test the stationarity of the Brent oil price before estimating ARIMA-BFGS. Evaluation measures such as root-mean-squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and coefficient of determination ( \(R^2\) R 2 ) are used to assess the performance of the models. The study utilises a combination of traditional methods and neural networks to improve the accuracy of the Brent oil price prediction. ANN-PSO improves the predictive precision of ARIMA-BFGS by 65.30% for the training dataset and 88.72% for the testing sample. The incorporation of COVID-19 and the Russia-Ukraine war has improved the performance of EVNN-FUZZY. Governments, investors and producers can all benefit from these outcomes while making financial decisions. The findings of this study can be used by oil-exporting economies to guide their budgets, while oil-importing countries can use them to manage inflation.