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Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/117730
The appraisal of machine learning techniques for tourism demand forecasting [Capítol de llibre]
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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.
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CLAVERÍA GONZÁLEZ, Óscar, MONTE MORENO, Enric and TORRA PORRAS, Salvador. The appraisal of machine learning techniques for tourism demand forecasting [Capítol de llibre]. Capítol del llibre: “Machine Learning: Advances in Research and Applications”. ISBN: 978-1-53612-570-2 Editors: Roger Inge and Jan Leif. Vol. Nova Science Publishers, num. 2017. [consulted: 9 of June of 2026]. Available at: https://hdl.handle.net/2445/117730