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Comparison of Artificial Neural Network Architectures in the Task of Tourism Time Series Forecast

Authors: João Paulo Teixeira, Paula Odete Fernandes

Abstract:

The authors have been developing several models based on artificial neural networks, linear regression models, Box- Jenkins methodology and ARIMA models to predict the time series of tourism. The time series consist in the “Monthly Number of Guest Nights in the Hotels" of one region. Several comparisons between the different type models have been experimented as well as the features used at the entrance of the models. The Artificial Neural Network (ANN) models have always had their performance at the top of the best models. Usually the feed-forward architecture was used due to their huge application and results. In this paper the author made a comparison between different architectures of the ANNs using simply the same input. Therefore, the traditional feed-forward architecture, the cascade forwards, a recurrent Elman architecture and a radial based architecture were discussed and compared based on the task of predicting the mentioned time series.

Keywords: Artificial Neural Network Architectures, time series forecast, tourism.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1072894

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References:


[1] Witt, Stephen F. and Witt, Christine A.. Forecasting tourism demand: a review of empirical research. International Journal of Forecasting. N.º 11, pp.447/475, 1995.
[2] Wong, K. F.. Introduction: Tourism Forecasting State of the Art. Journal of Travel and Tourism Marketing; N.º 13 (1/2), pp.1/3. 2002.
[3] Fernandes, Paula Odete. Modelling, Prediction and Behaviour Analysis of Tourism Demand in the North of Portugal. Ph.D. Thesis in Applied Economy and Regional Analysis. Valladolid University - Spain.. 2005.
[4] Yu, Gongmei and Schwartz, Zvi. Forecasting Short Time-Series Tourism Demand with Artificial Intelligence Models. Journal of Travel Research. N.º 45, pp. 194/203, 2006.
[5] Dolgner, R. & Costa, A.. Turismo, Sustentabilidade e Flexibilidade Laboral. 16º Congresso da APDR Universidade da Madeira, Funchal, pp. 801-818. 2010.
[6] Ministério da Economia e da Inova├º├úo. Plano Estratégico Nacional do Turismo - Para o desenvolvimento do Turismo em Portugal. Lisboa. 2006.
[7] WTO; United Nations World Tourism Organization, Tourism Market Trends.
[online]. UNWTO, 2006. Available in URL: http://www.unwto.org 02/2011.
[8] Thawornwong, S. and Enke, D.. The adaptive selection of financial and economic variables for use with artificial neural networks. Neurocomputing. N.º6, pp. 205/232. 2004.
[9] Hill, T.; O-connor, M. and Remus, W.. Neural network models for time series forecasts. Management Science. Vol. 42 (7), pp. 1082/1092. 1996.
[10] Hansen, J. V., Mcdonald, J. B. and Nelson, R. D.. Time series prediction with genetic-algorithm designed neural networks: an empirical comparison with modern statistical models. ComputlIntell. N.º15, pp. 171/184. 1999.
[11] Fernandes, P. and Teixeira, J.. A new approach to modelling and forecasting monthly overnights in the Northern Region of Portugal. Proceedings of the 15th International Finance Conference (CD-ROM); Université de Cergy; Hammamet, Medina, Tun├¡sia. 2007.
[12] Fernandes, Paula O.; Teixeira, Jo├úo Paulo - Applying the artificial neural network methodology to tourism time series forecasting. In 5th International Scientific Conference in ÔÇÿBusiness and Management. Vilnius, Lithuania. ISBN 978-9955-28-267-9. 2008.
[13] Teixeira, J. P. & Fernandes, P. O. A Insola├º├úo como Par├ómetro de Entrada em Modelo Baseado em Redes Neuronais para Previs├úo da Série Temporal do Turismo. CLME- 2011, Maputo.
[14] INE. Anuário Estatístico da Região Norte 2010. Instituto Nacional de Estatística, Lisboa. 2011.
[15] Bishop, C. M.. Neural Networks for pattern recognition. Oxford University Press. Oxford. London. 1995.
[16] Haykin, Simon. Neural Networks. A comprehensive foundation. New Jersey, Prentice Hall. 1999.
[17] Rumelhart, D. E. and McClelland, J. L.. Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Volume 1: Foundations. The Massachusetts Institute of Technology Press, Cambridge. 1986.
[18] Hagan, M. T. and Menhaj, M.. Training feedforward networks with the Marquardt algorithm, IEEE Transactions on Neural Networks, vol. 5, nº 6, pp.989-993. 1994.
[19] Donald Marquardt. An Algorithm for Least-Squares Estimation of Nonlinear Parameters. SIAM Journal on Applied Mathematics 11 (2): 431-441. 1963.
[20] Riedmiller, M. and Braun, H.. A direct adaptive method for faster backpropagation learning: The RPROP algorithm. Proceedings of the IEEE International Conference on Neural Networks. 1993.
[21] Demuth, H. and Beale, M.. Neural Network Toolbox, for use with Matlab - User-s Guide, version 4, by the Math Works. 2000.
[22] Fernandes, Paula O.; Teixeira, João Paulo - New approach of the ann methodology for forecasting time series: use of time index. In International Conference on Tourism Development and Management. Kos, Greece. 2011.
[23] Fernandes, Paula O.; Monte, Ana Paula; Teixeira, João Paulo - Previsão da procura turística utilizando um modelo não linear. In XIII Congreso Internacional de Investigación en Ciencias Administrativas. Mexico. 2009.