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dc.contributor.authorClavería González, Óscar-
dc.contributor.authorMonte Moreno, Enric-
dc.contributor.authorTorra Porras, Salvador-
dc.description.abstractMachine learning (ML) methods are being increasingly used with forecasting purposes. This study assesses the predictive performance of several ML models in a multiple-input multiple-output (MIMO) setting that allows incorporating the cross-correlations between the inputs. We compare the forecast accuracy of a Gaussian process regression (GPR) model to that of different neural network architectures in a multi-step-ahead time series prediction experiment. We find that the radial basis function (RBF) network outperforms the GPR model, especially for long-term forecast horizons. As the memory of the models increases, the forecasting performance of the GPR improves, suggesting the convenience of designing a model selection criteria in order to estimate the optimal number of lags used for
dc.format.extent22 p.-
dc.publisherNova Science Publishers, Inc.-
dc.relation.ispartofCapítol del llibre: “Machine Learning: Advances in Research and Applications”, ISBN: 978-1-53612-570-2 Editors: Roger Inge and Jan Leif, Nova Science Publishers, Inc. 2017. pp. 59-90-
dc.rights(c) Nova Science Publishers, Inc., 2017-
dc.subject.classificationAprenentatge automàtic-
dc.subject.classificationDistribució de Gauss-
dc.subject.classificationAnàlisi de regressió-
dc.subject.otherMachine learning-
dc.subject.otherGaussian distribution-
dc.subject.otherRegression analysis-
dc.titleThe appraisal of machine learning techniques for tourism demand forecasting [Capítol de llibre]ca
Appears in Collections:Llibres / Capítols de llibre (Econometria, Estadística i Economia Aplicada)

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