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Título:
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The appraisal of machine learning techniques for tourism demand forecasting [Capítol de llibre]
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Autor/a:
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Clavería González, Óscar; Monte Moreno, Enric; Torra Porras, Salvador
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Notas:
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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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Materia(s):
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-Aprenentatge automàtic
-Distribució de Gauss
-Anàlisi de regressió
-Previsió
-Machine learning
-Gaussian distribution
-Regression analysis
-Forecasting |
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Derechos:
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(c) Nova Science Publishers, Inc., 2017
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Tipo de documento:
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Capítulo o parte de libro
Artículo - Versión aceptada |
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Editor:
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Nova Science Publishers, Inc.
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Compartir:
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