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. 13
  3. Fernandez-Ceniceros, J. 2
  4. Pernia-Espinoza, A. 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

  3. 3 Tokyo Women's Medical University
    info

    Tokyo Women's Medical University

    Tokio, Japón

    ROR https://ror.org/03kjjhe36

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Zeitschrift:
Logic Journal of the IGPL

ISSN: 1367-0751

Datum der Publikation: 2017

Ausgabe: 25

Nummer: 6

Seiten: 877-889

Art: Artikel

DOI: 10.1093/JIGPAL/JZX029 SCOPUS: 2-s2.0-85042184229 WoS: WOS:000417432000003 GOOGLE SCHOLAR

Andere Publikationen in: Logic Journal of the IGPL

Projekte im Zusammenhang

SISTEMA INTELIGENTE EN TIEMPO REAL BASADO EN REDES DE SENSORES Y MINERÍA DE DATOS PARA LA TOMA DE DECISIONES EN PROCESOS DE CONSTRUCCIÓN DE EDIFICIOS E INFRAESTRUCTURAS (CONOBUILD)

2013/00131/001

Zusammenfassung

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. © The Author 2017.