Please use this identifier to cite or link to this item:
http://hdl.handle.net/2445/172665
Title: | The appraisal of machine learning techniques for tourism demand forecasting |
Author: | Clavería González, Óscar Monte Moreno, Enric Torra Porras, Salvador |
Keywords: | Aprenentatge automàtic Distribució de Gauss Anàlisi de regressió Xarxes neuronals convolucionals Machine learning Gaussian distribution Regression analysis Convolutional neural networks |
Issue Date: | 2017 |
Publisher: | Nova Science Publishers |
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. |
Note: | Versió postprint del document publicat a: https://search.proquest.com/docview/2190325008?pq-origsite=gscholar&fromopenview=true# |
It is part of: | International Journal of Computer Research, 2017, vol. 24, num. 2/3, p. 173-193 |
URI: | http://hdl.handle.net/2445/172665 |
ISSN: | 1535-6698 |
Appears in Collections: | Articles publicats en revistes (Econometria, Estadística i Economia Aplicada) |
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