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
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;
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
DOI :
10.1109/JICTEE.2014.6804078