Please use this identifier to cite or link to this item:
http://hdl.handle.net/2445/172665
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DC Field | Value | Language |
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dc.contributor.author | Clavería González, Óscar | - |
dc.contributor.author | Monte Moreno, Enric | - |
dc.contributor.author | Torra Porras, Salvador | - |
dc.date.accessioned | 2020-12-11T09:31:14Z | - |
dc.date.available | 2020-12-11T09:31:14Z | - |
dc.date.issued | 2017 | - |
dc.identifier.issn | 1535-6698 | - |
dc.identifier.uri | http://hdl.handle.net/2445/172665 | - |
dc.description.abstract | Machine learning (ML) methods are being increasingly used with forecasting purposes. This study assesses the predictive performance of several ML models in a multiple-input multiple-output (MIMO) setting that allows incorporating the cross-correlations between the inputs. We compare the forecast accuracy of a Gaussian process regression (GPR) model to that of different neural network architectures in a multi-step-ahead time series prediction experiment. 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.extent | 21 p. | - |
dc.format.mimetype | application/pdf | - |
dc.language.iso | eng | - |
dc.publisher | Nova Science Publishers | - |
dc.relation.isformatof | Versió postprint del document publicat a: https://search.proquest.com/docview/2190325008?pq-origsite=gscholar&fromopenview=true# | - |
dc.relation.ispartof | International Journal of Computer Research, 2017, vol. 24, num. 2/3, p. 173-193 | - |
dc.rights | (c) Nova Science Publishers, 2017 | - |
dc.source | Articles publicats en revistes (Econometria, Estadística i Economia Aplicada) | - |
dc.subject.classification | Aprenentatge automàtic | - |
dc.subject.classification | Distribució de Gauss | - |
dc.subject.classification | Anàlisi de regressió | - |
dc.subject.classification | Xarxes neuronals convolucionals | - |
dc.subject.other | Machine learning | - |
dc.subject.other | Gaussian distribution | - |
dc.subject.other | Regression analysis | - |
dc.subject.other | Convolutional neural networks | - |
dc.title | The appraisal of machine learning techniques for tourism demand forecasting | - |
dc.type | info:eu-repo/semantics/article | - |
dc.type | info:eu-repo/semantics/acceptedVersion | - |
dc.identifier.idgrec | 705063 | - |
dc.date.updated | 2020-12-11T09:31:14Z | - |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | - |
Appears in Collections: | Articles publicats en revistes (Econometria, Estadística i Economia Aplicada) |
Files in This Item:
File | Description | Size | Format | |
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705063.pdf | 460.13 kB | Adobe PDF | View/Open |
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