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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 (<jats:inline-formula>\n              <jats:alternatives>\n                <jats:tex-math>$$R^2$$<\/jats:tex-math>\n                <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:msup>\n                    <mml:mi>R<\/mml:mi>\n                    <mml:mn>2<\/mml:mn>\n                  <\/mml:msup>\n                <\/mml:math>\n              <\/jats:alternatives>\n            <\/jats:inline-formula>) 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. <\/jats:p>","DOI":"10.1007\/s00521-025-11306-2","type":"journal-article","created":{"date-parts":[[2025,5,29]],"date-time":"2025-05-29T13:47:43Z","timestamp":1748526463000},"page":"15661-15679","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Gaussian random fuzzy and nature-inspired neural networks: a novel approach to Brent oil price prediction"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1413-3974","authenticated-orcid":false,"given":"Sagiru","family":"Mati","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Goran Yousif","family":"Ismael","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abduallahi Garba","family":"Usman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmed","family":"Samour","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nazifi","family":"Aliyu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Raad Abdelhalim Ibrahim","family":"Alsakarneh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sani I.","family":"Abba","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,5,29]]},"reference":[{"issue":"11","key":"11306_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2023.e21439","volume":"9","author":"S Mati","year":"2023","unstructured":"Mati S, Radulescu M, Saqib N, Samour A, Ismael GY, Aliyu N (2023) Incorporating russo-ukrainian war in brent crude oil price forecasting: a comparative analysis of ARIMA, TARMA and ENNReg models. 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