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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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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

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Llibres / Capítols de llibre (Econometria, Estadística i Economia Aplicada)

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