Tourism demand modelling and forecasting with artificial neural network models: the Mozambique case study
Data(s) |
07/09/2016
07/09/2016
2016
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Resumo |
This 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. |
Identificador |
Constantino, H.; Fernandes, P. O.; Teixeira, João Paulo (2016) - Tourism demand modelling and forecasting with artificial neural network models: the Mozambique case study. Tékhne Review of Applied Management Studies. ISSN 1645-9911. 51 1645-9911 http://hdl.handle.net/10198/13193 10.1016/j.tekhne.2016.04.006 |
Idioma(s) |
eng |
Publicador |
Elsevier |
Direitos |
restrictedAccess http://creativecommons.org/licenses/by/4.0/ |
Palavras-Chave | #Mozambique #Artificial neural networks #Tourism demand #Forecasting #Modelling |
Tipo |
article |