A Hybrid Model of Artificial Neural Network and Genetic Algorithm in Forecasting Gold Price

Azme bin Khamis and Phang Hou Yee
Additional contact information
Azme bin Khamis: Department of Mathematics and Statistics, Faculty of Applied Science and Technology, Universiti Tun Hussein Onn Malaysia, Malaysia
Phang Hou Yee: Department of Mathematics and Statistics, Faculty of Applied Science and Technology, Universiti Tun Hussein Onn Malaysia, Malaysia

European Journal of Engineering and Technology Research, 2018, vol. 3, issue 6, 10-14

Abstract: The goal of this study is to compare the forecasting performance of classical artificial neural network and the hybrid model of artificial neural network and genetic algorithm. The time series data used is the monthly gold price per troy ounce in USD from year 1987 to 2016. A conventional artificial neural network trained by back propagation algorithm and the hybrid forecasting model of artificial neural network and genetic algorithms are proposed. Genetic algorithm is used to optimize the of artificial neural network neurons. Three forecasting accuracy measures which are mean absolute error, root mean squared error and mean absolute percentage error are used to compare the accuracy of artificial neural network forecasting and hybrid of artificial neural network and genetic algorithm forecasting model. Fitness of the model is compared by using coefficient of determination. The hybrid model of artificial neural network is suggested to be used as it is outperformed the classical artificial neural network in the sense of forecasting accuracy because its coefficient of determination is higher than conventional artificial neural network by 1.14%. The hybrid model of artificial neural network and genetic algorithms has better forecasting accuracy as the mean absolute error, root mean squared error and mean absolute percentage error is lower than the artificial neural network forecasting model.

Keywords: Artificial Neural Network; Forecasting; Genetic Algorithm; Gold Price; Machine Learning (search for similar items in EconPapers)
Date: 2018
References: Add references at CitEc
Citations:

Downloads: (external link)
https://eu-opensci.org/index.php/ejeng/article/view/60758 Abstract page (text/html)
https://eu-opensci.org/index.php/ejeng/article/download/60758/11905 Full text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:epw:ejeng0:v:3:y:2018:i:6:id:60758

DOI: 10.24018/ejeng.2018.3.6.758

Access Statistics for this article

More articles in European Journal of Engineering and Technology Research from European Open Science
Bibliographic data for series maintained by Support ().