Document type
Working paperPublication date
Publication license
Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/106074
Regional tourism demand forecasting with machine learning models : Gaussian process regression vs. neural network models in a multiple-input multiple-output setting
Journal Title
Director/Tutor
Journal ISSN
Volume Title
Related resource
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
Subject
Subject (English)
Citation
Citation
CLAVERÍA GONZÁLEZ, Óscar, MONTE MORENO, Enric and TORRA PORRAS, Salvador. Regional tourism demand forecasting with machine learning models : Gaussian process regression vs. neural network models in a multiple-input multiple-output setting. IREA – Working Papers. 2017. Vol. IR17/01. ISSN 2014-1254. [consulted: 17 of August of 2026]. Available at: https://hdl.handle.net/2445/106074