Tourism Demand Forecasting with Neural Network Models : Different Ways of Treating Information

@inproceedings{Claveria2015TourismDF,
  title={Tourism Demand Forecasting with Neural Network Models : Different Ways of Treating Information},
  author={Oscar Claveria and Enric Redondo Monte and Salvador Torra},
  year={2015}
}
This paper aims to compare the performance of three different artificial neural network techniques for tourist demand forecasting: a multi-layer perceptron, a radial basis function and an Elman network. We find that multi-layer perceptron and radial basis function models outperform Elman networks. We repeated the experiment assuming different topologies regarding the number of lags used for concatenation so as to evaluate the effect of the memory on the forecasting results. We find that for… CONTINUE READING

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