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Modelling Tourism Demand: A Comparative Study Between Artificial Neural Networks And The Box-Jenkins Methodology

Author

Listed:
  • Fernandez, Paula

    (Department of Economics and Management, Polytechnic Institute of Braganca (IPB), Portugal)

  • Teixeira, Joao

    (Department of Electrical Engineering, Polytechnic Institute of Bragança (IPB), Portugal)

  • Ferreira, Joao

    (Department of Management and Economics, University of Beira Interior (UBI), Portugal)

  • Azevedo, Susana G.

    (Department of Management and Economics, University of Beira Interior (UBI), Portugal)

Abstract

This study seeks to investigate and highlight the usefulness of the Artificial Neural Networks (ANN) methodology as an alternative to the Box-Jenkins methodology in analysing tourism demand. To this end, each of the above-mentioned methodologies is centred on the treatment, analysis and modelling of the tourism time series: “Nights Spent in Hotel Accommodation per Month”, recorded in the period from January 1987 to December 2006, since this is one of the variables that best expresses effective demand. The study was undertaken for the North and Centre regions of Portugal. The results showed that the model produced by using the ANN methodology presented satisfactory statistical and adjustment qualities, suggesting that it is suitable for modelling and forecasting the reference series, when compared with the model produced by using the Box?Jenkins methodology.

Suggested Citation

  • Fernandez, Paula & Teixeira, Joao & Ferreira, Joao & Azevedo, Susana G., 2008. "Modelling Tourism Demand: A Comparative Study Between Artificial Neural Networks And The Box-Jenkins Methodology," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 5(3), pages 30-50, Septembe2.
    • Handle: RePEc:rjr:romjef:v:5:y:2008:i:3:p:30-50
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    References listed on IDEAS

    as
    1. Witt, Stephen F. & Witt, Christine A., 1995. "Forecasting tourism demand: A review of empirical research," International Journal of Forecasting, Elsevier, vol. 11(3), pages 447-475, September.
    2. Tim Hill & Marcus O'Connor & William Remus, 1996. "Neural Network Models for Time Series Forecasts," Management Science, INFORMS, vol. 42(7), pages 1082-1092, July.
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    Cited by:

    1. Marcos Álvarez-Díaz & Manuel González-Gómez & María Soledad Otero-Giráldez, 2018. "Forecasting International Tourism Demand Using a Non-Linear Autoregressive Neural Network and Genetic Programming," Forecasting, MDPI, vol. 1(1), pages 1-17, September.

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    More about this item

    Keywords

    Artificial Neural Networks; ARIMA Models; Time Series Forecasting;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism

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