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Management Intelligent Systems pp 41–49Cite as

Training Neural Networks by Resilient Backpropagation Algorithm for Tourism Forecasting

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 220))

Abstract

The main objective of this study is to presents a set of models for tourism destinations competitiveness, using the Artificial Neural Networks (ANN) methodology. The time series of two regions (North and Centre of Portugal) has used to predict the tourism demand. The prediction for two years ahead gives a mean absolute percentage error between 5 and 9 %. Therefore, the ANN model is adequate for modelling and prediction of the reference time series. This model is an important and useful framework for better planning and development of these two regions as they operate in highly competitive markets.

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References

  1. Basheer, I., Hajmeer, M.: Artificial Neural Networks: fundamentals, computing, design and application. Journal of Microbiological Methods 153, 3–31 (2000)

    Article  Google Scholar 

  2. Bishop, C.M.: Neural Networks for pattern recognition. Oxford University Press, Oxford (1995)

    Google Scholar 

  3. Fernandes, P.: 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)

    Google Scholar 

  4. Fernandes, P., Teixeira, J.: A new approach to modelling and forecasting monthly overnights in the Northern Region of Portugal. In: Proceedings of the 4th International Finance Conference. Université de Cergy, Hammamet (2007)

    Google Scholar 

  5. Fernandes, P., Teixeira, J.: New Approach of the ANN Methodology for Forecasting Time Series: Use of Time Index. In: Proceeding of ICTDM, Kos-Greece (2009)

    Google Scholar 

  6. Fernandes, P., Teixeira, J., Ferreira, J., Azevedo, S.: Modelling Tourism Demand: A Comparative Study between Artificial Neural Networks and the Box-Jenkins Methodology. Journal of Economic Forecasting 5(3), 30–50 (2008)

    Google Scholar 

  7. Haykin, S.: Neural Networks. A comprehensive foundation. Prentice-Hall, New Jersey (1999)

    MATH  Google Scholar 

  8. Hill, T., O’connor, M., Remus, W.: Neural network models for time series forecasts. Management Science 42(7), 1082–1092 (1996)

    Article  MATH  Google Scholar 

  9. INE, National Institute of Portuguese Statistics, Lisbon (1987-2009)

    Google Scholar 

  10. Law, R.: Back-propagation learning in improving the accuracy of neural network-based tourism demand forecasting. Tourism Management 21, 331–340 (2000)

    Article  Google Scholar 

  11. Law, R., Au, N.: A neural network model to forecast Japanese demand for travel to Hong Kong. Tourism Management 20, 89–97 (1999)

    Article  Google Scholar 

  12. Lewis, C.: Industrial and Business Forecasting Method. Butterworth Scientific, London (1982)

    Google Scholar 

  13. Lin, C., Chen, H., Lee, T.: Forecasting Tourism Demand Using Time Series, Artificial Neural Networks and Multivariate Adaptive Regression Splines: Evidence from Taiwan. International Journal of Business Administration 2(2), 14–24 (2011)

    Article  Google Scholar 

  14. Machado, T., Teixeira, J., Fernandes, P.: Modelação da Procura Turística em Portugal - Regressão Linear versus Redes Neuronais Artificiais. Revista Turismo & Desenvolvimento 13, 325–335 (2010)

    Google Scholar 

  15. Muzaffer, U., Sherif, M.: Artificial Neural Networks versus Multiple Regression in Tourism Demand Analysis. Journal of Travel Research 38(2), 111–118 (1999)

    Article  Google Scholar 

  16. Palmer, A., Montano, J., Sese, A.: Designing an artificial neural network for forecasting tourism time series. Tourism Management 27, 781–790 (2006)

    Article  Google Scholar 

  17. Patti, C., Snyder, J.: Using a Neural Network to Forecast Visitor Behavior. Annals of Tourism Research 23(1), 151–164 (1996)

    Article  Google Scholar 

  18. Riedmiller, M., Braun, H.: A direct adaptive method for faster back-propagation learning: The RPROP algorithm. In: Proceedings of the IEEE International Conference on Neural Networks (1993)

    Google Scholar 

  19. Rumelhart, D., McClelland, J.: Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Foundations, vol. 1. The Massachusetts Institute of Technology Press, Cambridge (1986)

    Google Scholar 

  20. Teixeira, J., Fernandes, P.: A Insolação como Parâmetro de Entrada em Modelo Baseado em Redes Neuronais para Previsão da Série Temporal do Turismo. CLME, Maputo (2011)

    Google Scholar 

  21. Werbos, P.: Beyond regression: New tools for prediction and analysis in the behavioral sciences. Unpublished doctoral dissertation. Harvard University (1974)

    Google Scholar 

  22. Witt, S., Witt, C.: Forecasting tourism demand: a review of empirical research. International Journal of Forecasting 11, 447–475 (1995)

    Article  Google Scholar 

  23. Zhang, G., Patuwo, E., Hu, M.: Forecasting with artificial neural network: the state of the art. International Journal Forecasting 115, 35–62 (1998)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Polytechnic Institute of Bragança, Apartado 134, 5301-857, Bragança, Portugal

    Paula Odete Fernandes & João Paulo Teixeira

  2. NECE - Research Unit, University of Beira Interior, Covilhã, Portugal

    Paula Odete Fernandes, João Ferreira & Susana Azevedo

  3. University of Beira Interior, Pólo IV, Edifício Ernesto Cruz, 6200-209, Covilhã, Portugal

    João Ferreira & Susana Azevedo

Authors
  1. Paula Odete Fernandes
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  2. João Paulo Teixeira
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  3. João Ferreira
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  4. Susana Azevedo
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Corresponding author

Correspondence to Paula Odete Fernandes .

Editor information

Editors and Affiliations

  1. (CITIC-UGR), Department Computer Science and, University of Granada, Granada, 18071, Spain

    Jorge Casillas

  2. , Dept. Business Administration, University of Granada, Granada, 18071, Spain

    Francisco J. Martínez-López

  3. UFRGS, Department of Computer Systems, University of Sao Paulo, Sao Paulo, 91501-970, Brazil

    Rosa Vicari

  4. , Department of Computing Science, Universidad de Salamanca, Plaza de la Merced s/n, Salamanca, 37008, Spain

    Fernando De la Prieta

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© 2013 Springer International Publishing Switzerland

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Fernandes, P.O., Teixeira, J.P., Ferreira, J., Azevedo, S. (2013). Training Neural Networks by Resilient Backpropagation Algorithm for Tourism Forecasting. In: Casillas, J., Martínez-López, F., Vicari, R., De la Prieta, F. (eds) Management Intelligent Systems. Advances in Intelligent Systems and Computing, vol 220. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00569-0_6

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  • DOI: https://doi.org/10.1007/978-3-319-00569-0_6

  • Publisher Name: Springer, Heidelberg

  • Print ISBN: 978-3-319-00568-3

  • Online ISBN: 978-3-319-00569-0

  • eBook Packages: EngineeringEngineering (R0)

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