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

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  • Azme bin Khamis
  • Phang Hou Yee

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.

Suggested Citation

  • Azme bin Khamis & Phang Hou Yee, 2018. "A Hybrid Model of Artificial Neural Network and Genetic Algorithm in Forecasting Gold Price," European Journal of Engineering and Technology Research, European Open Science, vol. 3(6), pages 10-14, June.
    • Handle: RePEc:epw:ejeng0:v:3:y:2018:i:6:id:60758
    DOI: 10.24018/ejeng.2018.3.6.758
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    References listed on IDEAS

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    1. Shafiee, Shahriar & Topal, Erkan, 2010. "An overview of global gold market and gold price forecasting," Resources Policy, Elsevier, vol. 35(3), pages 178-189, September.
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