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dc.contributor.author | Clavería González, Óscar |
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dc.contributor.author | Monte Moreno, Enric |
dc.contributor.author | Torra Porras, Salvador |
dc.date | 2017-11-14T12:08:34Z |
dc.date | 2017-11-14T12:08:34Z |
dc.date | 2017 |
dc.identifier | http://hdl.handle.net/2445/117730 |
dc.identifier | 304795 |
dc.identifier.uri | http://hdl.handle.net/2445/117730 |
dc.description | 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 | 22 p. |
dc.format | application/pdf |
dc.language | eng |
dc.publisher | Nova Science Publishers, Inc. |
dc.relation | Capítol del llibre: “Machine Learning: Advances in Research and Applications”, ISBN: 978-1-53612-570-2 Editors: Roger Inge and Jan Leif, Nova Science Publishers, Inc. 2017. pp. 59-90 |
dc.rights | (c) Nova Science Publishers, Inc., 2017 |
dc.rights | info:eu-repo/semantics/openAccess |
dc.subject | Aprenentatge automàtic |
dc.subject | Distribució de Gauss |
dc.subject | Anàlisi de regressió |
dc.subject | Previsió |
dc.subject | Machine learning |
dc.subject | Gaussian distribution |
dc.subject | Regression analysis |
dc.subject | Forecasting |
dc.title | The appraisal of machine learning techniques for tourism demand forecasting [Capítol de llibre] |
dc.type | info:eu-repo/semantics/bookPart |
dc.type | info:eu-repo/semantics/acceptedVersion |