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

  1. Urraca, R. 2
  2. Sanz-Garcia, A. 1
  3. Fernandez-Ceniceros, J. 2
  4. Sodupe-Ortega, E. 2
  5. Martinez-De-pison, F.J. 2
  1. 1 University of Helsinki
    info

    University of Helsinki

    Helsinki, Finlandia

    ROR https://ror.org/040af2s02

  2. 2 Universidad de La Rioja
    info

    Universidad de La Rioja

    Logroño, España

    ROR https://ror.org/0553yr311

Revista:
Lecture Notes in Computer Science

ISSN: 0302-9743

Año de publicación: 2015

Volumen: 9121

Páginas: 632-643

Tipo: Artículo

DOI: 10.1007/978-3-319-19644-2_52 SCOPUS: 2-s2.0-84958550621 WoS: WOS:000363689900052 GOOGLE SCHOLAR

Otras publicaciones en: Lecture Notes in Computer Science

Resumen

This paper presents a hybrid methodology, in which a KDDscheme 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 output data, parameter tuning, and parsimonious model selection. In this work, experiments demonstrated that optimization of these steps 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. Results proved that the proposed method was useful to create models with higher generalization capacity and lower complexity to those obtained with classical KDD processes. © Springer International Publishing Switzerland 2015.