Optimization of neural network through genetic algorithm searches for the prediction of international crude oil price based on energy products prices

Abstract

This study investigated the prediction of crude oil price based on energy product prices using genetically optimized Neural Network (GANN). It was found from experimental evidence that the international crude oil price can be predicted based on energy product prices. The comparison of the prediction performance accuracy of the propose GANN with Support Vector Machine (SVM), Vector Autoregression (VAR), and Feed Forward NN (FFNN) suggested that the propose GANN was more accurate than the SVM, VAR, and FFNN in the prediction accuracy and time computational complexity. The propose GANN was able to improve the performance accuracy of the comparison algorithms. Our approach can easily be modified for the prediction of similar commodities.