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Si us plau utilitzeu sempre aquest identificador per citar o enllaçar aquest document: https://hdl.handle.net/2445/172665
The appraisal of machine learning techniques for tourism demand forecasting
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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, TORRA PORRAS, Salvador. The appraisal of machine learning techniques for tourism demand forecasting. _International Journal of Computer Research_. 2017. Vol. 24, núm. 2/3, pàgs. 173-193. [consulta: 24 de gener de 2026]. ISSN: 1535-6698. [Disponible a: https://hdl.handle.net/2445/172665]