Publication
Artificial neural networks versus Box Jenkins methodology in tourism demand analysis
| dc.contributor.author | Fernandes, Paula Odete | |
| dc.contributor.author | Teixeira, João Paulo | |
| dc.contributor.author | Ferreira, João José | |
| dc.contributor.author | Azevedo, Susana Garrido | |
| dc.date.accessioned | 2009-02-06T12:32:27Z | |
| dc.date.available | 2009-02-06T12:32:27Z | |
| dc.date.issued | 2008 | |
| dc.description.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 1 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. | en |
| dc.event.date | 2008 | en |
| dc.event.location | Liverpool, England | en |
| dc.event.title | 48th Congress of the European Regional Science Association | en |
| dc.event.type | Conferência | en |
| dc.identifier.citation | Fernandes, Paula O.; Teixeira, João Paulo; Ferreira, João José; Azevedo, Susana Garrido (2008). Artificial neural networks versus Box Jenkins methodology in tourism demand analysis. In 48th Congress of the European Regional Science Association. Liverpool, England. | en |
| dc.identifier.uri | http://hdl.handle.net/10198/1038 | |
| dc.language.iso | eng | en |
| dc.peerreviewed | yes | en |
| dc.publisher | University of Liverpool | en |
| dc.subject | Artificial neural networks | en |
| dc.subject | Backpropagation | en |
| dc.subject | Box-Jenkins methodology | en |
| dc.subject | Time series forecasting | en |
| dc.subject | Tourism demand | en |
| dc.title | Artificial neural networks versus Box Jenkins methodology in tourism demand analysis | en |
| dc.type | conference object | |
| dspace.entity.type | Publication | |
| person.familyName | Fernandes | |
| person.familyName | Teixeira | |
| person.givenName | Paula Odete | |
| person.givenName | João Paulo | |
| person.identifier | N-3804-2013 | |
| person.identifier | 663194 | |
| person.identifier.ciencia-id | 991D-9D1E-D67D | |
| person.identifier.ciencia-id | 4F15-B322-59B4 | |
| person.identifier.orcid | 0000-0001-8714-4901 | |
| person.identifier.orcid | 0000-0002-6679-5702 | |
| person.identifier.rid | N-6576-2013 | |
| person.identifier.scopus-author-id | 35200741800 | |
| person.identifier.scopus-author-id | 57069567500 | |
| rcaap.rights | openAccess | en |
| rcaap.type | conferenceObject | en |
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| relation.isAuthorOfPublication.latestForDiscovery | 2269147c-2b53-4d1c-bc1b-f1367d197262 |