A novel feature selection and short-term price forecasting based on a decision tree (J48) model

Abstract

A novel feature selection method based on a decision tree (J48) for price forecasting is proposed in this work. The method uses a genetic algorithm along with a decision tree classifier to obtain the minimum number of features giving an optimum forecast accuracy. The usefulness of the proposed approach is established through the performance test of the forecaster using the feature selected by this approach. It is found that the forecast with the selected feature consistently out-performed than that having larger feature set.