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Please use this identifier to cite or link to this item: http://hdl.handle.net/10198/7407

Título: Forecasting tourism demand with artificial neural networks
Autor: Fernandes, Paula O.
Teixeira, João Paulo
Ferreira, João José
Azevedo, Susana Garrido
Palavras-chave: Artificial neural networks
Nonlinear time series
Modelling
Tourism forecasting
Issue Date: 2011
Citação: Fernandes, Paula O.; Teixeira, João Paulo; Ferreira, João José; Azevedo, Susana Garrido (2011) - Forecasting tourism demand with artificial neural networks. In the International Conference on Tourism & Management Studies. Revista Encontros Científicos - Tourism & Management Studies. ISSN 1646-2408 7:2, Special Issue, p. 1014-1016.
Relatório da Série N.º: 7;
Resumo: Tourism has been viewed as an important player for the economic redevelopment of certain rural regions because of the attraction of landscapes, mountain, and the interest in second-home or investment opportunities at lower prices (Jackson & Murphy, 2002). Even with tourism‟s potential for development at all levels, there have been very few studies regarding models for estimating the local impact of tourism (Jackson & Murphy, 2006). To enhance understanding of the nature of forecasting in tourism destinations it is valuable to study systematically the possible estimative of influence that tourism destination has on an area. The main objective of this study is to present a set of models for tourism destinations competitiveness, using the Artificial Neural Networks methodology. This study focuses on two Portuguese regions - North and Centre - as tourism destinations offering a large range of tourist products, that goes beyond the beach, the mountains, the thermals not forgetting the rural tourism that has growing in the last years. These tourism destinations offer an interesting alternative to the „mass tourism‟ and have become more competitive, since the last one is normally associated with negative environmental impacts.
Arbitragem científica: yes
URI: http://hdl.handle.net/10198/7407
ISSN: 1646-2408
Appears in Collections:DEG - Artigos em Proceedings Não Indexados ao ISI

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