Artificial neural networks versus Box Jenkins methodology in tourism demand analysis
Conference Paper
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abstract
Several empirical studies in the tourism area have been performed and published during the last
decades. The researchers are unanimous upon considering that in the planning process, decisionmaking
and control of the tourism sector, the forecast of the tourism demand assumes an important
role.
Nowadays, there is a great variety of methods for forecasting that have been developed and which
can be applied in a set of situations presenting different characteristics and methodologies, going from
simple approaches to more complex ones.
In this context, the present study aims to explore and to evidence the usefulness of the Artificial Neural
Networks methodology (ANN), in the analysis of the tourism demand, as an alternative to the
Box-Jenkins methodology. ANN has been under attention in the area of business and economics
since, in this field, it presents this methodology as a valid alternative to classical methods of
forecasting allowing its application for problems in which the traditional ones would be difficult to use
(Thawornwong & Enke, 2004). As referred by Hill et al. (1996) and Hansen et al. (1999), ANN shows
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ability for improving time-series forecasts by mining additional information, diminishing their
dimensionality, and reducing their complexity. In this way, for each methodology treatment, analysis
and modeling of the tourism time-series: “Nights Spent in Hotel Accommodation per Month” registered
between January 1987 and December 2006, was carried out since is one of the variables that better
explains the effective tourism demand. The study was performed for the North and Center regions of
Portugal. Considering the results, and according to the Criteria of MAPE for model evaluation in Lewis
(1982), the ANN model presented an acceptable goodness of fit and good statistical properties and is,
therefore, adequate for modelling and prediction of the reference time series, when compared to the
results obtained by the methodology of Box-Jenkins.
