Forecasting Gold Prices Based on Extreme Learning Machine
Keywords:
Feed forward neural networks, Extreme Learning Machine, Gold price forecasting.Abstract
In recent years, the investors pay major attention to invest in gold market ecause of huge profits in the future. Gold is the only commodity which maintains ts value even in the economic and financial crisis. Also, the gold prices are closely elated with other commodities. The future gold price prediction becomes the warning ystem for the investors due to unforeseen risk in the market. Hence, an accurate gold rice forecasting is required to foresee the business trends. This paper concentrates on orecasting the future gold prices from four commodities like historical data’s of gold rices, silver prices, Crude oil prices, Standard and Poor’s 500 stock index (S&P500) ndex and foreign exchange rate. The period used for the study is from 1st January 000 to 31st April 2014. In this paper, a learning algorithm for single hidden layered eed forward neural networks called Extreme Learning Machine (ELM) is used which as good learning ability. Also, this study compares the five models namely Feed orward networks without feedback, Feed forward back propagation networks, Radial asis function, ELMAN networks and ELM learning model. The results prove that he ELM learning performs better than the other methods.
References
Gary Grudnitski, Larry Osburn, Forecasting S &P and Gold Futures Prices: An Application f Neural Networks, The Journal of Futures Markets, 13(6) : 631-643, 1993. http://dx.doi.org/10.1002/fut.3990130605
A.G. Malliaris and Mary Malliaris, Time Series and Neural Networks Comparison on Gold, il and the Euro, Proceedings of International Joint Conference on Neural Networks, Atlanta, eorgia, USA, June 14-19, 2009.
MehdiBijari, Gholam AliRaissi Ardali, Improvement of Auto-Regressive Integrated Moving verage models using Fuzzy logic and Artificial Neural Networks (ANNs), Neurocomputing, 2 : 956-967, 2009.
Ali ghezelbash, Predicting Changes in Stock Index and Gold prices to Neural Network approach, he Journal of Mathematics and Computer Science, 4 : 227-236, 2012.
Deepika M G, Gautam Nambiar and Rajkumar M, Forecasting price and analysing factors influencing he price of gold using ARIMA model and multiple regression analysis,International ournal of Research in Management, Economics and Commerce, 2(11) : Nov 2012.
Lazim Abdullah, ARIMA model for gold bullion coin selling prices forecasting, International ournal of Advances in Applied Sciences, 1(4) : 153-158, 2012.
Trian Hendro Asmoro, Exploring Gold Equivalency for forecasting steel prices on Pipeline rojects, 2(5) : 1-22, May 2013.
Dr. M. Massarrat Ali Khan, Forecasting of Gold Prices (Box Jenkins Approach), International ournal of Emerging Technology and Advanced Engineering, 3(3) : 2013.
Bai Li, Research on WNN Modelling for Gold Price Forecasting Based on Improved Artificial ee Colony Algorithm, Hindawi Publishing Corporation Computational Intelligence and euroscience Vol. 2014, pp. 1-10, 2014.
Fengyi Zhang and Zhigao Liao, Gold price forecasting based on RBF neural network and ybrid fuzzy clustering algorithm, Proceedings of the seventh International Conference on angement Science and Engineering Management, Lecture Notes in Electrical Engineering, ol. 1, pp. 73-84, 2014.
Hossein Mombeini and Abdolreza Yazdani-Chamzini, Modeling Gold price via Artificial eural Network, Journal of Economics, Business and Management, 3(7) : 2015.
Guang-Bin Huang, Lei Chen, Chee-Kheong Siew, Universal Approximation Using Incremental etworks with Random Hidden Computational Nodes IEEE Transactions on Neural etworks, 17(4) : 879-892, 2006.
Guang-Bin Huang, Qin-Yu Zhu, Chee-Kheong Siew, Extreme Learning Machine: Theory nd Applications,Neurocomputing, 70 : 489-501, 2006. http://dx.doi.org/10.1016/j.neucom.2005.12.126
Guang-Bin Huanga, Lei Chena, Convex Incremental Extreme Learning Machine, Neurocomputing, 0 : 3056-3062, 2007. http://dx.doi.org/10.1016/j.neucom.2007.02.009
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