Analysis of comparisons for forecasting gold price using neural network, radial basis function network and support vector regression

Khanoksin SuranartSupaporn KiattisinAdisorn LeelasantithamMahidol University2018-11-092018-11-092014-01-01JICTEE 2014 - 4th Joint International Conference on Information and Communication Technology, Electronic and Electrical Engineering. (2014)2-s2.0-84901044176https://repository.li.mahidol.ac.th/handle/20.500.14594/33845This 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. © 2014 IEEE.Mahidol UniversityEngineeringAnalysis of comparisons for forecasting gold price using neural network, radial basis function network and support vector regressionConference PaperSCOPUS10.1109/JICTEE.2014.6804078