This study proposes Gaussian processes to forecast daily hotel occupancy at a city level. Unlike other studies in the tourism demand prediction literature, the hotel occupancy rate is predicted on a daily basis and 45 days ahead of time using online hotel room price data. A predictive framework is introduced that highlights feature extraction and selection of the independent variables. This approach shows that the dependence on internal hotel occupancy data can be removed by making use of a proxy measure for hotel occupancy rate at a city level. Six forecasting methods are investigated, including linear regression, autoregressive integrated moving average and recent machine learning methods. The results indicate that Gaussian processes offer the best tradeoff between accuracy and interpretation by providing prediction intervals in addition to point forecasts. It is shown how the proposed framework improves managerial decision making in tourism planning.

中文翻译：

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旅游规划中日常需求预测的高斯过程

这项研究提出了高斯过程来预测城市级别的每日酒店入住率。与旅游需求预测文献中的其他研究不同，酒店入住率是使用在线酒店房价数据每天提前 45 天预测的。引入了一个预测框架，突出了自变量的特征提取和选择。这种方法表明，可以通过使用城市级别酒店入住率的代理措施来消除对内部酒店入住率数据的依赖。研究了六种预测方法，包括线性回归、自回归综合移动平均和最近的机器学习方法。结果表明，高斯过程通过提供除点预测之外的预测区间来提供准确性和解释之间的最佳折衷。展示了提议的框架如何改进旅游规划中的管理决策。