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
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
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
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)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:ira:wpaper:201701
Access Statistics for this paper
More papers in IREA Working Papers from University of Barcelona, Research Institute of Applied Economics Contact information at EDIRC.
Bibliographic data for series maintained by Alicia García ().