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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.


Keywords: Artificial Neural Network, Forecasting, Genetic Algorithm, Gold Price, Machine Learning

References

S. Shafiee and E. Topal, “An Overview of Global Gold Market and Gold Price Forecasting”, Resources Policy, 35(3), pp. 178-189, 2010.

H. Mombeini and A. Yazdani-Chamzini. “Modeling Gold Price Via Artificial Neural Network”. Journal of Economics, Business and Management, 3(7), pp. 699-703, 2015.

B. Li, “Research on WNN Modeling for Gold Price Forecasting Based on Improved Artificial Bee Colony Algorithm.” Computational intelligence and neuroscience, 2014(2), pp. 1-10 2014.

W. Kristjanpoller, and M. C. Minutolo, “Gold Price Volatility: A Forecasting Approach Using the Artificial Neural Network–Garch Model.” Expert Systems with Applications, 42(20), pp. 7245-7251, 2015.

Z. Ismail, A. Yahaya, and A. Shabri, “Forecasting Gold Prices Using Multiple Linear Regression Method.” American Journal of Applied Sciences, 6(8), pp. 1509-1514, 2009.

M. M. A. Khan, “Forecasting of Gold Prices (Box Jenkins Approach).” International Journal of Emerging Technology and Advanced Engineering, 3(3), pp. 662-670, 2003.

R. Davis, V. K. Dedu and F. Bonye, “Modeling and Forecasting of Gold Prices On Financial Markets.” Am. Int. J. Contemp. Res, 4(3), pp. 107-113, 2014.

H. Mombeini and A. Yazdani-Chamzini, “Modeling Gold Price Via Artificial Neural Network.” Journal of Economics, business and Management, 3(7), pp. 699-703, 2015.

H. Y. Yamin, S. M. Shahidehpour and Z. Li, “Adaptive Short-Term Electricity Price Forecasting Using Artificial Neural Networks in The Restructured Power Markets.” International journal of electrical power and energy systems, 26(8), pp. 571-581, 2014.

A. Khamis, Z. Ismail, K. Haron and A. T. Mohammed, “Neural Network Model for Oil Palm Yield Modeling.” Journal of Applied Sciences, 6(2), pp. 391-399, 2006.

D. J. Montana and L. Davis, “Training Feedforward Neural Networks Using Genetic Algorithms.” In IJCAI 89, pp. 762-767, 1989.

S. Mirmirani, and H. C. Li, “Gold Price, Neural Networks and Genetic Algorithm.” Computational Economics, 23(2), pp. 193-200, 2004.

K. J. Kim and I. Han, “Genetic Algorithms Approach to Feature Discretization in Artificial Neural Networks for the Prediction of Stock Price Index.” Expert systems with Applications, 19(2), pp. 125-132, 2000.

K. J. Kim and I. Han, “Application of A Hybrid Genetic Algorithm and Neural Network Approach in Activity-Based Costing." Expert Systems with Applications, 24(1), pp. 73-77, 2003.

M. Nasseri, K. Asghari, and M. J. Abedini, “Optimized Scenario For Rainfall Forecasting Using Genetic Algorithm Coupled With Artificial Neural Network”. Expert Systems with Applications, 35(3), pp. 1415-1421, 2008.

S. Haykin, and N. Network, “A Comprehensive Foundation.” Neural Networks, 2, pp. 41, 2004.

M. Mitchell, “An introduction to genetic algorithms.” England: MIT press, 1998.

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How to Cite
[1]
Khamis, A. and Yee, P. 2018. A Hybrid Model of Artificial Neural Network and Genetic Algorithm in Forecasting Gold Price. European Journal of Engineering Research and Science. 3, 6 (Jun. 2018), 10-14. DOI:https://doi.org/10.24018/ejers.2018.3.6.758.