Open AccessA Hybrid Model of Artificial Neural Network and Genetic Algorithm in Forecasting Gold Price
Author(s) -
Azme Khamis,
Phang Hou Yee
Publication year - 2018
Publication title -
european journal of engineering research and science
Language(s) - English
Resource type - Journals
ISSN - 2506-8016
DOI - 10.24018/ejers.2018.3.6.758
Subject(s) - artificial neural network , mean squared error , genetic algorithm , mean absolute percentage error , artificial intelligence , computer science , approximation error , algorithm , machine learning , mathematics , statistics
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.