dc.contributor.author | Jahanshahi, Hadi | |
dc.contributor.author | Uzun, Suleyman | |
dc.contributor.author | Kacar, Sezgin | |
dc.contributor.author | Yao, Qijia | |
dc.contributor.author | Alassafi, Madini O. | |
dc.date.accessioned | 2023-03-14T20:28:53Z | |
dc.date.available | 2023-03-14T20:28:53Z | |
dc.date.issued | 2022 | |
dc.identifier.issn | 2227-7390 | |
dc.identifier.uri | https://doi.org/10.3390/math10224361 | |
dc.identifier.uri | https://hdl.handle.net/20.500.14002/1489 | |
dc.description.abstract | The effect of the COVID-19 pandemic on crude oil prices just faded; at this moment, the Russia-Ukraine war brought a new crisis. In this paper, a new application is developed that predicts the change in crude oil prices by incorporating these two global effects. Unlike most existing studies, this work uses a dataset that involves data collected over twenty-two years and contains seven different features, such as crude oil opening, closing, intraday highest value, and intraday lowest value. This work applies cross-validation to predict the crude oil prices by using machine learning algorithms (support vector machine, linear regression, and rain forest) and deep learning algorithms (long short-term memory and bidirectional long short-term memory). The results obtained by machine learning and deep learning algorithms are compared. Lastly, the high-performance estimation can be achieved in this work with the average mean absolute error value over 0.3786. | en_US |
dc.description.sponsorship | Institutional Fund Projects [IFPDP-22622]; Ministry of Education and King Abdulaziz University (KAU), Jeddah, Saudi Arabia | en_US |
dc.description.sponsorship | This research work was funded by Institutional Fund Projects under Grant no. (IFPDP-22622). Therefore, the authors gratefully acknowledge technical and financial support from the Ministry of Education and King Abdulaziz University (KAU), Jeddah, Saudi Arabia. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Mdpi | en_US |
dc.relation.ispartof | Mathematics | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | prediction of crude oil prices | en_US |
dc.subject | COVID-19 effect | en_US |
dc.subject | Russia-Ukraine war effect | en_US |
dc.subject | machine learning | en_US |
dc.subject | deep learning | en_US |
dc.subject | time series forecasting | en_US |
dc.subject | Random Forest Classifier | en_US |
dc.title | Artificial Intelligence-Based Prediction of Crude Oil Prices Using Multiple Features under the Effect of Russia-Ukraine War and COVID-19 Pandemic | en_US |
dc.type | article | en_US |
dc.authorid | UZUN, Suleyman/0000-0001-8246-6733 | |
dc.authorid | YAO, Qijia/0000-0001-7902-407X | |
dc.authorid | Alassafi, Madini O./0000-0001-9919-8368 | |
dc.department | Belirlencek | en_US |
dc.identifier.doi | 10.3390/math10224361 | |
dc.identifier.volume | 10 | en_US |
dc.identifier.issue | 22 | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.identifier.wos | WOS:000887475300001 | en_US |
dc.identifier.scopus | 2-s2.0-85142499468 | en_US |