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

Chiroma, Haruna and Ya’u Gital, Abdulsalam and Abubakar, Adamu and Usman, Mohammed Joda and Waziri, Usman (2014) Optimization of neural network through genetic algorithm searches for the prediction of international crude oil price based on energy products prices. In: CF '14 Proceedings of the 11th ACM Conference on Computing Frontiers, 20-22 May 2014, Cagliari, Italy .

PDF - Published Version Restricted to Repository staff only Download (201kB) | Request a copy

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.

Item Type:	Conference or Workshop Item (UNSPECIFIED)
Additional Information:	7132/37900 ISBN: 978-1-4503-2870-8
Uncontrolled Keywords:	Genetic algorithm, Neural Network, Crude oil price
Subjects:	T Technology > T Technology (General)
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Information and Communication Technology > Department of Information System Kulliyyah of Information and Communication Technology > Department of Information System
Depositing User:	Ahmad Nazreen Mohd Shamsuri (PT)
Date Deposited:	23 Sep 2014 09:50
Last Modified:	12 Jun 2018 15:36
URI:	http://irep.iium.edu.my/id/eprint/37900

Actions (login required)

View Item