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Title: | The appraisal of machine learning techniques for tourism demand forecasting [Capítol de llibre] |
Author: | Clavería González, Óscar Monte Moreno, Enric Torra Porras, Salvador |
Keywords: | Aprenentatge automàtic Distribució de Gauss Anàlisi de regressió Previsió Machine learning Gaussian distribution Regression analysis Forecasting |
Issue Date: | 2017 |
Publisher: | Nova Science Publishers, Inc. |
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. |
It is part of: | 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 |
URI: | http://hdl.handle.net/2445/117730 |
Appears in Collections: | Llibres / Capítols de llibre (Econometria, Estadística i Economia Aplicada) |
Files in This Item:
File | Description | Size | Format | |
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Book Machine Learning (2017) - Chapter 2 - pp 59-90 - postprint.pdf | 395.08 kB | Adobe PDF | View/Open |
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