Percorrer por autor "Azevedo, Susana Garrido"
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- Artificial neural networks versus Box Jenkins methodology in tourism demand analysisPublication . Fernandes, Paula Odete; Teixeira, João Paulo; Ferreira, João José; Azevedo, Susana GarridoSeveral 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.
- Forecasting tourism demand with artificial neural networksPublication . Fernandes, Paula Odete; Teixeira, João Paulo; Ferreira, João José; Azevedo, Susana GarridoTourism 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.
- Forecasting tourism demand with artificial neural networksPublication . Fernandes, Paula Odete; Teixeira, João Paulo; Ferreira, João José; Azevedo, Susana GarridoTourism 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.
- O Impacto da variável páscoa na previsão da procura turísticaPublication . Fernandes, Paula Odete; Teixeira, João Paulo; Ferreira, João José; Azevedo, Susana Garrido
- O impacto da variável Páscoa na previsão da procura turísticaPublication . Fernandes, Paula Odete; Teixeira, João Paulo; Ferreira, João José; Azevedo, Susana GarridoCom este estudo pretende-se analisar o impacto do feriado móvel da Páscoa na previsão da procura turística, para as regiões Norte e Centro de Portugal. De salientar que a série temporal “Dormidas Mensais registadas nos estabelecimentos hoteleiros”, considerada como significativa da actividade turística, devido às suas características, denota que os fenómenos influenciam de forma distinta a procura turística, nas regiões em estudo. Assim, tendo por base modelos não lineares, sustentados pela metodologia das Redes Neuronais Artificiais (RNA), vai-se verificar se os resultados sofreram alterações significativas antes e após a utilização da variável dummy Páscoa. A inclusão desta nova variável no modelo prende-se com o facto de se ter detectado, em estudos anteriores, alguns valores atípicos na série temporal, dormidas mensais nos estabelecimentos hoteleiros nas regiões em estudo, pelo que se tenciona captar esse efeito.
- Modelação da procura turística: um estudo comparativo entre redes neuronais artificiais e a metodologia de Box-JenkinsPublication . Fernandes, Paula Odete; Teixeira, João Paulo; Ferreira, João José; Azevedo, Susana GarridoThe present research aims to explore and to evidence the utility of the methodology of Artificial Neural Networks (ANN) in the analysis of tourism demand as an alternative to the Box-Jenkins methodology. The first methodology has arising interest in the economic and business area since several researches have verified that methodology presents a valid alternative to classical methods of forecasting allowing giving answer to situations in which the traditional ones will be of difficult to apply (Thawornwong & Enke, 2004). According to Hill et al. (1996) and Hansen et al. (1999) ANN show capacity to improve the time-series forecasts through of additional information analysis decreasing their dimension and reducing their complexity. For that, each one of the referred methodologies focused in the treatment, analysis and modeling of the tourism time-series: Monthly Guest Nights in Hotels registered between January 1987 to December 2006, since it is one of the variables that better explain the effective tourism demand. The Study was performed for two regions of Portugal: North region and Centre region. Considering the results, and according to the Criteria of MAPE for model evaluation proposed by Lewis (1982), the ANN model presented acceptable statistical qualities and adjustments satisfied. Being so, it is adequate not only for the modelling but also to the prediction of times series, when compared to the model performed by Box- Jenkins methodology. We intended also to evaluate the performance and competiveness of the tourism destinations - North region and Center region of Portugal - by main origin markets and to analyse how it is distributed their portfolio of origin markets for the period of 1997 to 2006. The Market Share Analysis tool proposed by Faulkner (1997) was applied and it was observed an high dependency of the domestic market for both regions.
- Modelling tourism demand: a comparative study between artificial neural networks and the Box-Jenkins methodologyPublication . Fernandes, Paula Odete; Teixeira, João Paulo; Ferreira, João José; Azevedo, Susana GarridoThis 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.
- Training neural networks by resilient backpropagation algorithm for tourism forecastingPublication . Fernandes, Paula Odete; Teixeira, João Paulo; Ferreira, João José; Azevedo, Susana GarridoThe main objective of this study is to presents a set of models for tourism destinations competitiveness, using the Artificial Neural Networks (ANN) methodology. The time series of two regions (North and Centre of Portugal) has used to predict the tourism demand. The prediction for two years ahead gives a mean absolute percentage error between 5 and 9 %. Therefore, the ANN model is adequate for modelling and prediction of the reference time series. This model is an important and useful framework for better planning and development of these two regions as they operate in highly competitive markets.