Forecasting gold prices based on extreme learning machine

Title

Forecasting gold prices based on extreme learning machine

Creator

Chandar S.K.; Sumathi M.; Sivanadam S.N.

Description

In recent years, the investors pay major attention to invest in gold market because of huge profits in the future. Gold is the only commodity which maintains its value even in the economic and financial crisis. Also, the gold prices are closely related with other commodities. The future gold price prediction becomes the warning system for the investors due to unforeseen risk in the market. Hence, an accurate gold price forecasting is required to foresee the business trends. This paper concentrates on forecasting the future gold prices from four commodities like historical data's of gold prices, silver prices, Crude oil prices, Standard and Poor's 500 stock index (S & P500) index and foreign exchange rate. The period used for the study is from 1st January 2000 to 31st April 2014. In this paper, a learning algorithm for single hidden layered Feed forward neural networks called Extreme Learning Machine (ELM) is used which has good learning ability. Also, this study compares the five models namely Feed forward networks without feedback, Feed forward back propagation networks, Radial basis function, ELMAN networks and ELM learning model. The results prove that the ELM learning performs better than the other methods. 2006-2016 by CCC Publications.

Source

International Journal of Computers, Communications and Control, Vol-11, No. 3, pp. 372-380.

Date

2016-01-01

Publisher

Agora University

Subject

Extreme learning machine; Feed forward neural networks; Gold price forecasting

Coverage

Chandar S.K., Christ University, Bangalore, India; Sumathi M., Sri Meenakshi Government College For Arts For Women (Autonomous), Madurai, India; Sivanadam S.N., Karpagam College Of Engineering, Coimbatore, India

Rights

All Open Access; Gold Open Access

Relation

ISSN: 18419836

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.15837/ijccc.2016.3.2009

https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962439513&doi=10.15837%2fijccc.2016.3.2009&partnerID=40&md5=fa9701d9ed74c52a195f9c916daddd6e