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

Clavería González, ÓscarMonte Moreno, EnricTorra Porras, Salvador2017-01-252017-01-2520172014-1254https://hdl.handle.net/2445/106074This 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 concatenationapplication/pdfengcc-by-nc-nd, (c) Clavería González et al., 2017http://creativecommons.org/licenses/by-nc-nd/3.0/Previsió econòmicaTurismeEconomic forecastingTourismRegional tourism demand forecasting with machine learning models : Gaussian process regression vs. neural network models in a multiple-input multiple-output settinginfo:eu-repo/semantics/workingPaper2017-01-25info:eu-repo/semantics/openAccess