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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 |
Keywords: | Previsió econòmica Turisme Economic forecasting Tourism |
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: | AQR (Grup d’Anàlisi Quantitativa Regional) – Working Papers Documents de treball (Institut de Recerca en Economia Aplicada Regional i Pública (IREA)) |
Files in This Item:
File | Description | Size | Format | |
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IR17-001_Claveria_RegionalTourism.pdf | 1.26 MB | Adobe PDF | View/Open |
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