Apply decision tree and support vector regression to predict the gold price
Ongsritrakul, P.
Soonthornphisaj, N.
Dept. of Comput. Sci., Kasetsart Univ., Bangkok, Thailand;
This paper appears in: Neural Networks, 2003. Proceedings of the International Joint Conference on
Publication Date: 20-24 July 2003
Volume: 4,
On page(s): 2488- 2492 vol.4
ISSN: 1098-7576
ISBN: 0-7803-7898-9
INSPEC Accession Number: 8597100
Digital Object Identifier: 10.1109/IJCNN.2003.1223955
Current Version Published: 2003-08-26
Abstract
Recently, support vector regression (SVR) was proposed to resolve time series prediction and regression problems. In this paper, we demonstrate the use of SVR techniques for predicting the cost of gold by using factors that have an effect on gold to estimate its price. We apply a decision tree algorithm for the feature selection task and then perform the regression process using forecasted indexes. Our experimental results show that the combination of the decision tree and SVR leads to a better performance.
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