Optimized Multivariate Adaptive Regression Splines for Predicting Crude Oil Demand in Saudi Arabia
This paper presents optimized linear regression with multivariate adaptive regression splines (LR-MARS) for predicting crude oil demand in Saudi Arabia based on social spider optimization (SSO) algorithm. The SSO algorithm is applied to optimize LR-MARS performance by fine-tuning its hyperparameters...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2022/8412895 |
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| author | Eman H. Alkhammash Abdelmonaim Fakhry Kamel Saud M. Al-Fattah Ahmed M. Elshewey |
| author_facet | Eman H. Alkhammash Abdelmonaim Fakhry Kamel Saud M. Al-Fattah Ahmed M. Elshewey |
| author_sort | Eman H. Alkhammash |
| collection | DOAJ |
| description | This paper presents optimized linear regression with multivariate adaptive regression splines (LR-MARS) for predicting crude oil demand in Saudi Arabia based on social spider optimization (SSO) algorithm. The SSO algorithm is applied to optimize LR-MARS performance by fine-tuning its hyperparameters. The proposed prediction model was trained and tested using historical oil data gathered from different sources. The results suggest that the demand for crude oil in Saudi Arabia will continue to increase during the forecast period (1980–2015). A number of predicting accuracy metrics including Mean Absolute Error (MAE), Median Absolute Error (MedAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and coefficient of determination (R2) were used to examine and verify the predicting performance for various models. Analysis of variance (ANOVA) was also applied to reveal the predicting result of the crude oil demand in Saudi Arabia and also to compare the actual test data and predict results between different predicting models. The experimental results show that optimized LR-MARS model performs better than other models in predicting the crude oil demand. |
| format | Article |
| id | doaj-art-b26b3d67e6054fe595d84a5adaeddcd6 |
| institution | OA Journals |
| issn | 1607-887X |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-b26b3d67e6054fe595d84a5adaeddcd62025-08-20T02:23:19ZengWileyDiscrete Dynamics in Nature and Society1607-887X2022-01-01202210.1155/2022/8412895Optimized Multivariate Adaptive Regression Splines for Predicting Crude Oil Demand in Saudi ArabiaEman H. Alkhammash0Abdelmonaim Fakhry Kamel1Saud M. Al-Fattah2Ahmed M. Elshewey3Department of Computer ScienceFaculty of Graduate Environmental StudiesSaudi AramcoFaculty of Computers and InformationThis paper presents optimized linear regression with multivariate adaptive regression splines (LR-MARS) for predicting crude oil demand in Saudi Arabia based on social spider optimization (SSO) algorithm. The SSO algorithm is applied to optimize LR-MARS performance by fine-tuning its hyperparameters. The proposed prediction model was trained and tested using historical oil data gathered from different sources. The results suggest that the demand for crude oil in Saudi Arabia will continue to increase during the forecast period (1980–2015). A number of predicting accuracy metrics including Mean Absolute Error (MAE), Median Absolute Error (MedAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and coefficient of determination (R2) were used to examine and verify the predicting performance for various models. Analysis of variance (ANOVA) was also applied to reveal the predicting result of the crude oil demand in Saudi Arabia and also to compare the actual test data and predict results between different predicting models. The experimental results show that optimized LR-MARS model performs better than other models in predicting the crude oil demand.http://dx.doi.org/10.1155/2022/8412895 |
| spellingShingle | Eman H. Alkhammash Abdelmonaim Fakhry Kamel Saud M. Al-Fattah Ahmed M. Elshewey Optimized Multivariate Adaptive Regression Splines for Predicting Crude Oil Demand in Saudi Arabia Discrete Dynamics in Nature and Society |
| title | Optimized Multivariate Adaptive Regression Splines for Predicting Crude Oil Demand in Saudi Arabia |
| title_full | Optimized Multivariate Adaptive Regression Splines for Predicting Crude Oil Demand in Saudi Arabia |
| title_fullStr | Optimized Multivariate Adaptive Regression Splines for Predicting Crude Oil Demand in Saudi Arabia |
| title_full_unstemmed | Optimized Multivariate Adaptive Regression Splines for Predicting Crude Oil Demand in Saudi Arabia |
| title_short | Optimized Multivariate Adaptive Regression Splines for Predicting Crude Oil Demand in Saudi Arabia |
| title_sort | optimized multivariate adaptive regression splines for predicting crude oil demand in saudi arabia |
| url | http://dx.doi.org/10.1155/2022/8412895 |
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