The large amounts of hospitality and tourism-related search data sampled at different frequencies have long presented a challenge for hospitality and tourism demand forecasting. This study aims to evaluate the applicability of large panels of search series sampled at daily frequencies to improve the forecast precision of monthly hotel demand. In particular, a hybrid mixed-data sampling regression approach integrating a dynamic factor model and forecast combinations is the first reported method to incorporate mixed-frequency data while remaining parsimonious and flexible. A case study is undertaken by investigating Sanya, the southernmost city in Hainan province, as a tourist destination using 9 years of the experimental data set. Dynamic factor analysis is used to extract the information from large panels of web search series, and forecast combinations are attempted to obtain the final prediction results of the individual forecasts to enhance the prediction accuracy further. The empirical analysis results suggest that the developed hybrid forecast approach leads to improvements in monthly forecasts of hotel occupancy over its competitors.

长期以来，以不同频率采样的大量与酒店和旅游相关的搜索数据一直为酒店和旅游需求预测提出了挑战。这项研究旨在评估以每日频率抽样的大型搜索系列面板的适用性，以提高对每月酒店需求的预测精度。特别是，将动态因子模型和预测组合相结合的混合混合数据采样回归方法是第一个报告的方法，该方法在保持简约和灵活的同时并入了混合频率数据。通过使用9年的实验数据集，对海南省最南端的城市三亚作为旅游胜地进行了案例研究。动态因素分析用于从大型网络搜索系列面板中提取信息，尝试使用预测组合和预测组合来获得各个预测的最终预测结果，以进一步提高预测准确性。实证分析结果表明，发展起来的混合预测方法可以提高其竞争对手酒店入住率的月度预测。