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Title: The appraisal of machine learning techniques for tourism demand forecasting
Author: Clavería González, Óscar
Monte Moreno, Enric
Torra Porras, Salvador
Keywords: Aprenentatge automàtic
Distribució de Gauss
Anàlisi de regressió
Xarxes neuronals convolucionals
Machine learning
Gaussian distribution
Regression analysis
Convolutional neural networks
Issue Date: 2017
Publisher: Nova Science Publishers
Abstract: 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.
Note: Versió postprint del document publicat a:
It is part of: International Journal of Computer Research, 2017, vol. 24, num. 2/3, p. 173-193
ISSN: 1535-6698
Appears in Collections:Articles publicats en revistes (Econometria, Estadística i Economia Aplicada)

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