香港旅游需求预测的稀疏高斯过程回归模型摘要 近年来，高斯过程（GP）模型已被广泛研究，努力解决机器学习问题。能 够灵活的使用默瑟内核和概率推理的贝叶斯框架的非参数化建模能力的模型是 很重要的。在本文中,我们提出一个稀疏 GP 回归(GPR)在香港旅游需求预测模型。 探地雷达的 sparsification 过程模型不仅降低了计算复杂度,而且提高了泛化 能力。我们实验所提出的模型是适用于香港的旅游业月度需求数据，以及稀疏 GPR 模型的性能与各种基于内核的模式进行比较，以显示其有效性。稀疏 GPR 模型建议显示，其预测能力优于那些 ARMA 模型和国家的最先进的两种 SVM 模型。1.引言 在国际入境旅游需求方面，无论是在旅游收入和旅游人数上，香港旅游业 最近都发生了剧烈的变化。与这些翻天覆地的变化相关的是入境旅游市场的结 构调整,因此对旅游产品和服务的需求不同。作为一个例子，对个人游计划在中 国内地某城市居民推出后在香港旅游需求的变化更为突出(Law, To, Law, 2000b; Law, Goh, Mok, 1990; Witt Quinonero-Candela Law, 2000a; Uysal Law,2000b; Law Fan, Chen, Guo Xu, Law, Xu Kim Lathiras Lee, Var, Lim, 1997; Song 香港理工大学（G-YX5J）中国博士后科学基金 （20090451152） ，江苏省规划项目的博士后研究基金（0901023C）和东南大学 规划项目的博士后研究基金支持。参考文献Brahim-Belhouari, S., & Bermak, A. (2004). Gaussian process for nonstationary time series prediction. Computational Statistics and Data Analysis, 47(4), 705712. Chen, K. Y., &Wang, C. H. (2007). Support vector regression with genetic algorithms in forecasting tourism demand. Tourism Management, 28(1), 215226. Cho, V. (2003). A comparison of three different approaches to tourist arrival forecasting. Tourism Management, 24(3), 323330. Cressie, N. (1993). Statistics for spatial data. New York: John Wiley & Sons. Cristianini, N., & Schlkopf, B. (2002). Support vector machines and kernel methods:The new generation of learning machines. AI Magazine, 23(3), 3142. Fan, R.-E., Chen, P.-H., & Lin, C.-J (2005). Working set selection using the second order information for training SVM. Technical report, Department of Computer Science,National Taiwan University. Goh, C., & Law, R. (2003). Incorporating the rough sets theory into travel demand analysis. Tourism Management, 24(5), 511517. Goh, C., Law, R., & Mok, H. M. K. (2008). Analyzing and forecasting tourism demand:A rough sets approach. Journal of Travel Research, 46(3), 327338.Guo, G. D., & Li, S. Z. (2003). Content-based audio classification and retrieval by support vector machines. IEEE Transactions on Neural Networks, 14(1), 209215. Keerthi, S. S., & Chu, W. (2006). A matching pursuit approach to sparse GP regression. In Y. Weiss, B. Schlkopf, & J. Platt (Eds.). Advances in neural information processing systems (Vol. 18, pp. 643 650). Cambridge, MA: MIT Press. NIPS18. Kim, Y., & Uysal, M. (1998). Time-dependent analysis for international hotel demand in Seoul. Tourism Economics, 4(3), 253 263. Lathiras, P., & Siriopoulos, C. (1998). The demand for tourism to Greece: A cointegration approach. Tourism Economics, 4(2), 171185. Law, R. (2000a). Demand for hotel spending by visitors to Hong Kong: A study of various forecasting techniques. Journal of Hospitality and Leisure Marketing, 6(4),1729. Law, R. (2000b). Back-propagation learning in improving the accuracy of neural network-based tourism demand forecasting. Tourism Management, 21(3),331340. Law, R., & Au, N. (1999). A neural network model to forecast Japanese demand for travel to Hong Kong. Tourism Management, 20(1), 8997. Law, R., Goh, C., & Pine, R. (2004). Modeling tourism demand: A decision rules based approach. Journal of Travel and Tourism Marketing, 16(2/3), 6169. Law, R., To, T., & Goh, C. (2008). How do mainland Chinese travelers choose restaurants in Hong Kong? An exploratory study of Individual Visit Scheme travelers and packaged travelers. International Journal of Hospitality Management, 27(3), 346354.Lawrence, S., & Giles, C. (2000). Overfitting and neural networks: Conjugate gradient and back-propagation. In Proceedings of the IEEE international conference on neural networks (pp. 114119). Lee, C. K., Var, T., & Blaine, T. W. (1996). Determinants of inbound tourist expenditures. Annals of Tourism Research, 23(3), 527542.Lim, C. (1997). An econometric classification and review of international tourism demand models. Tourism Economics, 3(1), 6981. Lin, C. J. (2001). Formulations of support vector machines: A note from an optimization point of view. Neural Computation, 13(2), 307317. Mok, H. M. K. (1990). A quasi-experimental measure of the arc elasticities of destinational advertising: Some evidence from Hawaii. Journal of Travel Research, 3034. Neal, R. M. (1996). Bayesian learning for neural networks. Lecture notes in statistics. Springer. Pai, P. F., & Hong, W. C. (2005). An improved neural network model in forecasting arrivals. Annals of Tourism Research, 32(4), 11381141. Perez, E. A., & Sampol, C. J. (2000). Tourist expenditure for mass tourism markets.Annals of Tourism Research, 27(3), 624637. Quinonero-Candela, J., & Rasmussen, C. E. (2005). A unifying view of sparse approximate Gaussian process regression. Journal of Machine Learning Research,6, 19391959. Rasmussen, C. E. (1996). Evaluation of gaussian processes and other methods for non-linear regression. PhD thesis, Department of Computer Science, University of Toronto, Toronto, Ontario. Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian processes for machine learning.Cambridge, Massachus