Please use this identifier to cite or link to this item:
http://hdl.handle.net/2445/106074
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Clavería González, Óscar | - |
dc.contributor.author | Monte Moreno, Enric | - |
dc.contributor.author | Torra Porras, Salvador | - |
dc.date.accessioned | 2017-01-25T12:54:46Z | - |
dc.date.available | 2017-01-25T12:54:46Z | - |
dc.date.issued | 2017 | - |
dc.identifier.issn | 2014-1254 | - |
dc.identifier.uri | http://hdl.handle.net/2445/106074 | - |
dc.description.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 | - |
dc.format.mimetype | application/pdf | - |
dc.language.iso | eng | - |
dc.publisher | Universitat de Barcelona. Institut de Recerca en Economia Aplicada Regional i Pública | - |
dc.relation.ispartof | IREA – Working Papers, 2017, IR17/01 | - |
dc.relation.ispartof | AQR – Working Papers, 2017, AQR17/01 | - |
dc.relation.ispartofseries | [WP E-AQR17/01] | - |
dc.relation.ispartofseries | [WP E-IR17/01] | - |
dc.rights | cc-by-nc-nd, (c) Clavería González et al., 2017 | - |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/ | - |
dc.source | Documents de treball (Institut de Recerca en Economia Aplicada Regional i Pública (IREA)) | - |
dc.subject.classification | Previsió econòmica | - |
dc.subject.classification | Turisme | - |
dc.subject.other | Economic forecasting | - |
dc.subject.other | Tourism | - |
dc.title | Regional tourism demand forecasting with machine learning models : Gaussian process regression vs. neural network models in a multiple-input multiple-output setting | - |
dc.type | info:eu-repo/semantics/workingPaper | - |
dc.date.updated | 2017-01-25T12:54:46Z | - |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | - |
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 | |
---|---|---|---|---|
IR17-001_Claveria_RegionalTourism.pdf | 1.26 MB | Adobe PDF | View/Open |
This item is licensed under a
Creative Commons License