Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/106074
Title: Regional tourism demand forecasting with machine learning models : Gaussian process regression vs. neural network models in a multiple-input multiple-output setting
Author: Clavería González, Óscar
Monte Moreno, Enric
Torra Porras, Salvador
Issue Date: 2017
Publisher: Universitat de Barcelona. Institut de Recerca en Economia Aplicada Regional i Pública
Series/Report no: [WP E-AQR17/01]
[WP E-IR17/01]
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
It is part of: IREA – Working Papers, 2017, IR17/01
AQR – Working Papers, 2017, AQR17/01
URI: http://hdl.handle.net/2445/106074
ISSN: 2014-1254
Appears in Collections:Documents de treball (Institut de Recerca en Economia Aplicada Regional i Pública (IREA))
AQR (Grup d’Anàlisi Quantitativa Regional) – Working Papers

Files in This Item:
File Description SizeFormat 
IR17-001_Claveria_RegionalTourism.pdf1.26 MBAdobe PDFView/Open


This item is licensed under a Creative Commons License Creative Commons