Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/23700
Title: A sparse gaussian process regression model for tourism demand forecasting in Hong Kong
Authors: Wu, Q
Law, R 
Xu, X
Keywords: Sparse Gaussian process
Support vector machine
Tourism demand forecasting
Kernel machines
Issue Date: 2012
Publisher: Pergamon Press
Source: Expert systems with applications, 2012, v. 39, no. 5, p. 4769-4774 How to cite?
Journal: Expert systems with applications 
Abstract: In recent years, Gaussian process (GP) models have been popularly studied to solve hard machine learning problems. The models are important due to their flexible non-parametric modeling abilities using Mercer kernels and the Bayesian framework for probabilistic inference. In this paper, we propose a sparse GP regression (GPR) model for tourism demand forecasting in Hong Kong. The sparsification procedure of the GPR model not only decreases the computational complexity but also improves the generalization ability. We experiment the proposed model with monthly demand data that are relevant to Hong Kong's tourism industry, and compare the performance of the sparse GPR model with those of various kernel-based models to show its effectiveness. The proposed sparse GPR model shows that its forecasting capability outperforms those of the ARMA model and the two state-of-the-art SVM models.
URI: http://hdl.handle.net/10397/23700
ISSN: 0957-4174
EISSN: 1873-6793
DOI: 10.1016/j.eswa.2011.09.159
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