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Orientador(es)
Resumo(s)
The modulation of tourism time series was used in this work for forecast purposes.
The Tourism Revenue and Total Overnights registered in the hotels of the North region of Por-
tugal were used for the experimented models. Several feed-forward Artificial Neural Networks
(ANN) models using different input features and number of hidden nodes were experimented
to forecast the Tourism time series. Empirical results indicate that the Dedicated ANN models
perform better than models with several outputs. Generally the usage of previous 12 values of
the same time series is very important to a good quality forecast. For the prediction of Tourism
Revenue the Foreign Overnights and GDP of contributing countries are relevant. This time series
was predicted with an error of 4.7% and a Pearson correlation of 0.98. The forecast of Total
Overnights had an error of 6.0% and Pearson correlation of 0.98. Domestic Overnights are more
predictable than Foreign Overnights.
Descrição
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Contexto Educativo
Citação
Teixeira, João Paulo; Fernandes, Paula O. (2014). Tourism time series forecast with artificial neural networks. Tékhne, Review of Applied Management Studies. ISSN 1645-9911. 12:1-2, p. 26–36
Editora
Elsevier