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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

Oscar Claveria (), Enric Monte () and Salvador Torra ()
Additional contact information
Enric Monte: Department of Signal Theory and Communications, Polytechnic University of Catalunya (UPC).
Salvador Torra: Riskcenter-IREA, University of Barcelona, Av. Diagonal 690, 08034 Barcelona, Spain.

No 201701, IREA Working Papers from University of Barcelona, Research Institute of Applied Economics

Abstract: 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.

Keywords: Regional forecasting; tourism demand; multiple-input multiple-output (MIMO); Gaussian process regression; neural networks; machine learning. JEL classification: (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cmp, nep-for and nep-tur
Date: 2017-01, Revised 2017-01
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Working Paper: Regional tourism demand forecasting with machine learning models: Gaussian process regression vs. neural network models in a multiple-input multiple-output setting (2017) Downloads
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