Evolutionary Neural Network model for West Texas Intermediate crude oil price prediction

Abstract

This paper proposes an alternative approach based on a genetic algorithm and neural network (GA-NN) for the prediction of the West Texas Intermediate (WTI) crude oil price. Comparative simulation results suggested that the proposed GA-NN approach is better than the baseline algorithms in terms of prediction accuracy and computational efficiency. Mann-Whitney test results indicated that the WTI crude oil price predicted by the proposed GA-NN and the observed price are statistically equal. Further comparison of the proposed GA-NN with previous studies indicated performance improvement over existing results. The proposed model can be useful in the formulation of policies related to international crude oil price estimations, development plans and industrial production.