DocumentCode
133342
Title
Analysis of comparisons for Forecasting Gold Price using Neural Network, Radial Basis Function Network and Support Vector Regression
Author
Suranart, Khanoksin ; Kiattisin, Supapom ; Leelasantitham, Adisom
Author_Institution
Fac. of Eng., Mahidol Univ., Nakhon Pathom, Thailand
fYear
2014
fDate
5-8 March 2014
Firstpage
1
Lastpage
5
Abstract
This research is done to study and analyze the comparison for forecasting the gold price using Neural Network, Radial Basis Function Network, and Support Vector Regression. Which the neural network radial basis function network and support vector regression is a method of learning about the machine by using the details of the of the short term prices of the gold. The duration of this short term price is from June in the year 2008 until April 2013, the details collected will be broken up into two parts, which is Monthly details, and weekly detail. The details of the monthly detail will be predicted 3 months ahead and for the weekly details will be predicted 3 weeks ahead. The details will be measured for accuracy by the deviation, the complete average value, average squared error, average error, and the absolute average error value.
Keywords
financial data processing; gold; pricing; radial basis function networks; regression analysis; support vector machines; time series; absolute average error value; average error; average squared error; complete average value; deviation; gold price forecasting; learning method; monthly details; neural network; radial basis function network; support vector regression; weekly details; Educational institutions; Equations; Gold; Neurons; Radial basis function networks; Support vector machines; Neural Network; Radial Basis Function Network; Support Vector Regression; forecast; gold price;
fLanguage
English
Publisher
ieee
Conference_Titel
Information and Communication Technology, Electronic and Electrical Engineering (JICTEE), 2014 4th Joint International Conference on
Conference_Location
Chiang Rai
Print_ISBN
978-1-4799-3854-4
Type
conf
DOI
10.1109/JICTEE.2014.6804078
Filename
6804078
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