@article{392,
author = "Buah Ahoba Masha",
abstract = "In this study five Artificial Neural Network (ANN) models namely Back propagation Neural Network (BPNN), Radial Basis Function Neural Network (RBFNN), Least Squares Support Vector Machines (LSSVM), Generalized Regression Neural Network (GRNN) and Group Method Data Handling (GMDH) were developed to predict oil consumption of the Organization of Petroleum Exporting Countries (OPEC) and their predictive performance were compared. The results of the study showed that, RBFNN model out performed all the developed models with MSE, VAF, PI, MSE values of 0.00707, 82.8538,0.85038,3.9899 and 1.532 respectively. The performance of the RBFNN was closely followed by GMDH, BPNN, LSSVM and GRNN. Based on the results of the Sobol’s sensitivity analysis, values of oil export was the most influential factor that affects oil consumption of OPEC member states.",
issn = "23942894",
journal = "IJASM",
keywords = "Artificial Neural Network;Oil Consumption;Macroeconomic Indicators;OPEC",
month = "May",
number = "3",
pages = "47-60",
title = "{O}il {C}onsumption {P}rediction of {OPEC} {M}ember {S}tates using {A}rtificial {N}eural {N}etwork",
volume = "9",
year = "2022",
}