Abstract
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 ( R 2 ) 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.
Highlights
Macias-Diaz is 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. e SSO algorithm is applied to optimize LR-MARS performance by fine-tuning its hyperparameters. e proposed prediction model was trained and tested using historical oil data gathered from different sources. e results suggest that the demand for crude oil in Saudi Arabia will continue to increase during the forecast period (1980–2015)
In [13], three estimated models for the price of petroleum called theories model, simulation model, and informal model were used. e informal estimate model performs better results than the other two models. e authors in [14] make use of eight artificial neural networks (ANN) and fuzzy regression (FR) for oil price prediction. e analysis of variance (ANOVA) and Duncan’s multiple range test (DMRT) are used to test the forecast produced by ANN and FR. e mean absolute percentage error (MAPE) was calculated for ANN models and the results have shown that ANN models outperform the FR models
SSO has been used to perform tuning to the two hyperparameters (penalty term and maximum number of basis functions (BFs)). e population of SSO metaheuristic algorithm consists of 30 members and the
Summary
Macias-Diaz is 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. Analysis of variance (ANOVA) was applied to reveal the predicting result of the crude oil demand in Saudi Arabia and to compare the actual test data and predict results between different predicting models. 1. Introduction e development of prediction techniques and machine learning models is a critical task for crude oil demand [1]. Many techniques have been presented in the field of regression analysis, which can be divided into parametric method and nonparametric method. Because more information is available, the ability to predict new values is more flexible because the parameters in the nonparametric method have infinite dimensions, and the data characteristics are superior to parametric models
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