A sparse Gaussian process regression model for tourism demand forecasting in Hong Kong

Authors:
Qi Wu;Rob Law;Xin Xu
Affiliations:
Key Laboratory of Measurement and Control of CSE (School of Automation, Southeast University), Ministry of Education, Nanjing, Jiangsu 210096, China and School of Hotel and Tourism Management, Hon ...;School of Hotel and Tourism Management, Hong Kong Polytechnic University, Hung Horn, Kowloon, Hong Kong;Institute of Automation, College of Mechatronics and Automation, National University of Defense Technology, Changsha 410073, China
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

Citing 10
Cited 1

The nature of statistical learning theory

The nature of statistical learning theory
Support vector machines and Kernel methods: the new generation of learning machines

AI Magazine
Evaluation of gaussian processes and other methods for non-linear regression

Evaluation of gaussian processes and other methods for non-linear regression
Formulations of Support Vector Machines: A Note from an Optimization Point of View

Neural Computation
A Unifying View of Sparse Approximate Gaussian Process Regression

The Journal of Machine Learning Research
Classification of Business Travelers Using SVMs Combined with Kernel Principal Component Analysis

ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
The forecasting model based on wavelet ν-support vector machine

Expert Systems with Applications: An International Journal
A hybrid-forecasting model based on Gaussian support vector machine and chaotic particle swarm optimization

Expert Systems with Applications: An International Journal
An adaptive network intrusion detection method based on PCA and support vector machines

ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
Content-based audio classification and retrieval by support vector machines

IEEE Transactions on Neural Networks

Tourism demand forecasting using novel hybrid system

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	12.05

Visualization

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.