Apply decision tree and support vector regression to predict the gold price
Ongsritrakul, P.; Soonthornphisaj, N.
Neural Networks, 2003. Proceedings of the International Joint Conference on
Volume 4, Issue , 20-24 July 2003 Page(s): 2488 - 2492 vol.4
Digital Object Identifier   10.1109/IJCNN.2003.1223955
Summary: 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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