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Regional tourism demand forecasting with machine learning models : Gaussian process regression vs. neural network models in a multiple-input multiple-output setting
Clavería González, Óscar; Monte Moreno, Enric; Torra Porras, Salvador
This study presents a multiple-input multiple-output (MIMO) approach for multi-step-ahead time series prediction with a Gaussian process regression (GPR) model. We assess the forecasting performance of the GPR model with respect to several neural network architectures. The MIMO setting allows modelling the cross-correlations between all regions simultaneously. 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
cc-by-nc-nd, (c) Clavería González et al., 2017
http://creativecommons.org/licenses/by-nc-nd/3.0/
Working Paper
Universitat de Barcelona. Institut de Recerca en Economia Aplicada Regional i Pública
         

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