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

机译：香港旅游需求预测的稀疏高斯过程回归模型

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摘要

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.
机译：近年来，高斯过程（GP）模型已得到广泛研究，以解决硬机器学习问题。这些模型之所以重要，是因为它们具有使用Mercer内核和贝叶斯框架进行概率推断的灵活的非参数建模能力。在本文中，我们提出了一种稀疏的GP回归（GPR）模型来预测香港的旅游需求。 GPR模型的稀疏化过程不仅降低了计算复杂度，而且提高了泛化能力。我们使用与香港旅游业相关的月度需求数据对提议的模型进行了实验，并将稀疏的GPR模型与各种基于核的模型的性能进行了比较，以证明其有效性。提出的稀疏GPR模型表明，其预测能力优于ARMA模型和两个最新的SVM模型。

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  • 来源
    《Expert Systems with Application》 |2012年第5期|p.4769-4774|共6页
  • 作者

    Qi Wu; Rob Law; Xin Xu;

  • 作者单位

    Key Laboratory of Measurement and Control of CSE (School of Automation, Southeast University), Ministry of Education, Nanjing, Jiangsu 210096, China,School of Hotel and Tourism Management, Hong Kong Polytechnic University, Hung Horn, Kowloon, Hong Kong;

    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;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    sparse gaussian process; support vector machine; tourism demand forecasting; kernel machines;

    机译：稀疏的高斯过程;支持向量机旅游需求预测;内核机器;

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