MODELLING TOURISM DEMAND: A COMPARATIVE STUDY BETWEEN ARTIFICIAL NEURAL NETWORKS AND THE BOX-JENKINS METHODOLOGY
Paula Fernandez (),
Joao Teixeira (),
Joao Ferreira and
Susana Azevedo ()
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Paula Fernandez: Department of Economics and Management, Polytechnic Institute of Braganca (IPB), Portugal
Joao Teixeira: Department of Electrical Engineering, Polytechnic Institute of Bragança (IPB), Portugal
Authors registered in the RePEc Author Service: Paula Odete Fernandes ()
Journal for Economic Forecasting, 2008, vol. 5, issue 3, 30-50
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.
Keywords: Artificial Neural Networks; ARIMA Models; Time Series Forecasting (search for similar items in EconPapers)
JEL-codes: C01 C02 C22 C45 L83 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:rjr:romjef:v:5:y:2008:i:3:p:30-50
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