Abstract
This paper 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 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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Weatherford, L.R., Kimes, S.E.: A comparison of forecasting methods for hotel revenue management. Int. J. Forecast. 19(3), 401–415 (2003)
Zickus, J.: Forecasting for airline network revenue management: Revenue and competitive impacts (1998)
Aziz, H.A., Saleh, M., Rasmy, M.H., ElShishiny, H.: Dynamic room pricing model for hotel revenue management systems. Egypt. Inf. J. 12(3), 177–183 (2011)
Anjos, M.F., Cheng, R.C., Currie, C.S.: Optimal pricing policies for perishable products. Eur. J. Oper. Res. 166(1), 246–254 (2005)
Sparks, B.A., Browning, V.: The impact of online reviews on hotel booking intentions and perception of trust. Tour. Manag. 32(6), 1310–1323 (2011)
Cantoni, L., Fans, M., Inversini, A., Passini, V.: Hotel websites and booking engines: a challenging relationship. In: Law, R., Fuchs, M., Ricci, F. (eds.) Information and Communication Technologies in Tourism 2011 (Proceedings of the Int Conf. in Innsbruck, Austria), pp. 241–252. Springer, New York (2011)
Lai, K.K., Ng, W.L.: A stochastic approach to hotel revenue optimization. Comput. Oper. Res. 32(5), 1059–1072 (2005)
Haensel, A., Koole, G.: Booking horizon forecasting with dynamic updating: a case study of hotel reservation data. Int. J. Forecast. 27, 942–960 (2011)
Yang, Y.Y., Mahfouf, M., Pnoutsos, G.: Development of a parsimonious ga-nn ensemble model with a case study for charpy impact energy prediction. Adv. Eng. Softw. 42, 435–443 (2011)
Sanz-Garcia, A., Fernández-Ceniceros, J., Fernández-MartÃnez, R., MartÃnez-De-Pisón, F.J.: Methodology based on genetic optimisation to develop overall parsimony models for predicting temperature settings on annealing furnace. Ironmak. Steelmaking 41(2), 87–98 (2014)
Yang, Y., Linkens, D., Talamantes-Silva, J.: Roll load prediction - data collection, analysis and neural network modelling. J. Mater. Process. Technol. 152(3), 304–315 (2004)
Helle, M., Saxen, H., Kerkkonen, O.: Assessment of the state of the blast furnace high temperature region by tuyere core drilling. ISIJ Int. 49(2), 203–209 (2009)
Drucker, H., Chris, K.B.L., Smola, A., Vapnik, V.: Support vector regression machines. In: Advances in Neural Information Processing Systems 9, vol. 9, pp. 155–161 (1997)
Vapnik, V.N.: Statistical Learning Theory, 1st edn. Wiley, New York (1998)
Box, G.E.P., Cox, D.R.: An analysis of transformations. J. Roy. Stat. Soc.: Ser. B (Methodol.) 26(2), 211–252 (1964)
Ye, J.: On measuring and correcting the effects of data mining and model selection. J. Am. Stat. Assoc. 93(441), 120–131 (1998)
Seni, G., Elder, J.: Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions. Morgan and Claypool Publishers, Chicago (2010)
Michalewicz, Z., Janikow, C.Z.: Handling constraints in genetic algorithms. In: ICGA, pp. 151–157 (1991)
R Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2013)
Acknowledgements
We are greatly indebted to Banco Santander for the PROFAI-13/06 fellowship, and to the Agencia de Desarrollo Económico de La Rioja for the ADER-2012-I-IDD-00126 (CONOBUILD) fellowship and to the Instituto de Estudios Riojanos (IER) for funding parts of this research. We would also like to convey our gratitude to the European Union for its continuous encouragement through the \(7^{th}\) Framework Programme on the project VINEROBOT. And, one of the authors, ASG, would also like to acknowledge research founding No. 273689 (FINSKIN) and the mobility grant No. 276371 (VATURP) from the Academy of Finland.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Urraca, R., Sanz-Garcia, A., Fernandez-Ceniceros, J., Sodupe-Ortega, E., Martinez-de-Pison, F.J. (2015). Improving Hotel Room Demand Forecasting with a Hybrid GA-SVR Methodology Based on Skewed Data Transformation, Feature Selection and Parsimony Tuning. In: Onieva, E., Santos, I., Osaba, E., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2015. Lecture Notes in Computer Science(), vol 9121. Springer, Cham. https://doi.org/10.1007/978-3-319-19644-2_52
Download citation
DOI: https://doi.org/10.1007/978-3-319-19644-2_52
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19643-5
Online ISBN: 978-3-319-19644-2
eBook Packages: Computer ScienceComputer Science (R0)