The appraisal of machine learning techniques for tourism demand forecasting [Capítol de llibre]
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 acc...
| Autores: | , , |
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| Tipo de recurso: | capítulo de libro |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2017 |
| País: | España |
| Institución: | Universidad de Barcelona |
| Repositorio: | Dipòsit Digital de la UB |
| OAI Identifier: | oai:diposit.ub.edu:2445/117730 |
| Acceso en línea: | https://hdl.handle.net/2445/117730 |
| Access Level: | acceso abierto |
| Palabra clave: | Aprenentatge automàtic Distribució de Gauss Anàlisi de regressió Previsió Machine learning Gaussian distribution Regression analysis Forecasting |
| Sumario: | 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. |
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