2026-04-17T15:55:48Zhttps://recercat.cat/oai/request

oai:recercat.cat:2445/1177302025-12-05T05:49:21Zcom_2072_1057col_2072_478808col_2072_478917

The appraisal of machine learning techniques for tourism demand forecasting [Capítol de llibre] Clavería González, Óscar Monte Moreno, Enric Torra Porras, Salvador Aprenentatge automàtic Distribució de Gauss Anàlisi de regressió Previsió Machine learning Gaussian distribution Regression analysis Forecasting Machine 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 concatenation. 2017-11-14T12:08:34Z 2017-11-14T12:08:34Z 2017 info:eu-repo/semantics/bookPart info:eu-repo/semantics/acceptedVersion https://hdl.handle.net/2445/117730 304795 eng Capítol 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 (c) Nova Science Publishers, Inc., 2017 info:eu-repo/semantics/openAccess 22 p. application/pdf Nova Science Publishers, Inc. Llibres / Capítols de llibre (Econometria, Estadística i Economia Aplicada)