Improving hotel room demand forecasting with a hybrid GA-SVR methodology based on skewed data transformation, feature selection and parsimony tuning

This article presents a hybrid methodology in which a KDD scheme is optimized to build accurate parsimonious models. The methodology tries to find the best model by using genetic algorithms to optimize a KDD scheme formed with the following stages: feature selection, transformation of the skewed input and the output data, parameter tuning and parsimonious model selection. The results obtained demonstrated the optimization of these steps that significantly improved the model generalization capabilities in some UCI databases. Finally, this methodology was applied to create room demand parsimonious models using booking databases from a hotel located in a region of Northern Spain. The results proved that the proposed method created models with higher generalization capacity and lower complexity compared to those obtained with classical KDD process.