Journal
ENERGIES
Volume 12, Issue 19, Pages -Publisher
MDPI
DOI: 10.3390/en12193665
Keywords
price forecasting; J48 classifier; feature selection; elite genetic algorithm; confidence interval
Categories
Funding
- Technical Education Quality Improvement Program (TEQIP-III), IET, Dr. Rammanohar Lohia Avadh University, Ayodhya
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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.
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