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The appraisal of machine learning techniques for tourism demand forecasting

# metadata.dc.description.abstract[].value
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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International Journal of Computer Research, 2017, vol. 24, num. 2/3, p. 173-193

# metadata.dc.source[].value
Articles publicats en revistes  (Econometria, Estadística i Economia Aplicada)

# metadata.dc.title[].value
The appraisal of machine learning techniques for tourism demand forecasting