Forecasting Gold Prices Based on Extreme Learning Machine

  • Kumar Chandar Sivalingam CHRIST University
  • Sumathi Mahendran Sri Meenakshi Government College for Arts for Women
  • Sivanandam Natarajan Karpagam College Of Engineering,


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.

Author Biographies

Kumar Chandar Sivalingam, CHRIST University
Associate ProforessorDepartment of Management Studies Christ University
Sumathi Mahendran, Sri Meenakshi Government College for Arts for Women
Associate Professor, Department of Computer Science
Sivanandam Natarajan, Karpagam College Of Engineering,
Professor Emeritus, Department of Computer Science & Engineering


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How to Cite
SIVALINGAM, Kumar Chandar; MAHENDRAN, Sumathi; NATARAJAN, Sivanandam. Forecasting Gold Prices Based on Extreme Learning Machine. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 11, n. 3, p. 372-380, mar. 2016. ISSN 1841-9844. Available at: <>. Date accessed: 07 july 2020. doi:


Feed forward neural networks, Extreme Learning Machine, Gold price forecasting.