# name
Regional tourism demand forecasting with machine learning models : Gaussian process regression vs. neural network models in a multiple-input multiple-output setting

# metadata.dc.description.abstract[].value
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

# metadata.dc.publisher[].value
Universitat de Barcelona. Institut de Recerca en Economia Aplicada Regional i Pública

# metadata.dc.rights[].value
cc-by-nc-nd, (c) Clavería González et al., 2017

# metadata.dc.source[].value
Documents de treball (Institut de Recerca en Economia Aplicada Regional i Pública (IREA))

# metadata.dc.title[].value
Regional tourism demand forecasting with machine learning models : Gaussian process regression vs. neural network models in a multiple-input multiple-output setting