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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.
- Tourism demand modeling and forecasting with artificial neural models: the Mozambique case studyPublication . Constantino, Hortêncio; Fernandes, Paula Odete; Teixeira, João PauloThis study aimed to model and forecast the tourism demand for Mozambique for the period from January 2004 to December 2013 using Artificial Neural Networks models. The number of overnight stays in Hotels was used as representative of the tourism demand. This variable was used as the output of the model.
- Applying the artificial neural network methodology for forecasting the tourism time seriesPublication . Fernandes, Paula Odete; Teixeira, João Paulo
- 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.
- A comparison of linear and non linear models to forecast the tourism demand in the North of PortugalPublication . Natália, Santos; Fernandes, Paula Odete; Teixeira, João PauloIn order to contribute for enriching studies in the tourism field, it was intended with this research paper performing the comparison between the model based on linear regression and the model based on artificial neural networks and analyses of the performance of those models. Additionally, the usefulness of the time series that measures the number of hours of Sunshine should be confirmed. We used for this purpose the monthly series that measures the demand for tourism: “Monthly Nights in Hotels in the Northern Region of Portugal”, recorded in the period from January 1990 to December 2009. A linear regression model based on the first differences was developed producing none statistical infractions. A previously developed ANN based model was applied for the new period of time under comparison. Both models have the sunshine time series in their entrance. Both methodologies proved to achieve similarly good results in getting the seasonality of the time series, because the correlation coefficient was at the level of 0.99. Also both models could predict with high quality the magnitude of the time series because the mean absolute percentage error was 4.1% and 3.5% for the linear model and for the ANN based model, respectively.
- Tourism demand modelling and forecasting with artificial neural network models: the Mozambique case studyPublication . Constantino, Hortêncio; Fernandes, Paula Odete; Teixeira, João PauloThis study is aimed to model and forecast the tourism demand for Mozambique for the period from January 2004 to December 2013 using artificial neural networks models. The number of overnight stays in Hotels was used as representative of the tourism demand. A set of independent variables were experimented in the input of the model, namely: Consumer Price Index, Gross Domestic Product and Exchange Rates, of the outbound tourism markets, South Africa, United State of America, Mozambique, Portugal and the United Kingdom. The best model achieved has 6.5% for Mean Absolute Percentage Error and 0.696 for Pearson correlation coefficient. A model like this with high accuracy of forecast is important for the economic agents to know the future growth of this activity sector, as it is important for stakeholders to provide products, services and infrastructures and for the hotels establishments to adequate its level of capacity to the tourism demand.
- 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.
- Prediction tourism demand using artificial neural networksPublication . Fernandes, Paula Odete; Teixeira, João PauloThe aim of this research is to quantify the tourism demand using an Artificial Neural Network (ANN) model. The methodology was focused in the treatment, analysis and modulation of the tourism time series: “Monthly Guest Nights in Hotels” in Northern Portugal recorded between January 1987 and December 2006, since it is one of the variables that better explain the effective tourism demand. The model used 4 neurons in the hidden layer with the logistic activation function and was trained using the Resilient Backpropagation algorithm. Each time series forecast depended on 12 preceding values. The developed model yielded acceptable goodness of fit and statistical properties and therefore it is adequate for the modulation and prediction of the reference time series.