2026-04-13T16:51:47Zhttps://recercat.cat/oai/request

oai:recercat.cat:2117/275232026-02-03T01:42:19Zcom_2072_1033col_2072_452950

00925njm 22002777a 4500 dc Claveria, Oscar author Monte Moreno, Enrique author Torra Porras, Salvador author 2014-07-21 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 higher memories, the forecasting performance obtained for longer horizons improves, suggesting the importance of increasing the dimensionality for long-term forecasting. Peer Reviewed Postprint (published version) Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial Àrees temàtiques de la UPC::Informàtica Neural networks (Computer science) Perceptrons Tourism demand Forecasting Artificial neural networks Multi-layer perceptron Radial basis function Elman networks Xarxes neuronals (Informàtica) Perceptrons Tourism demand forecasting with neural network models: different ways of treating information