Accurate forecasting of the hotel accommodation demands is extremely critical to the sustainable development of tourism-related industries. In view of the ever-increasing tourism data, this paper constructs a deep learning framework to handle the prediction problem in the hotel accommodation demands. Taking China's Hainan province as an empirical example, the internet search index is used from August 2008 to May 2019 to forecast the overnight passenger flows for hotels accommodation in Hainan Province, China. Forecasting results indicate that compared to benchmark models, the constructed forecasting method can effectively simulate dynamic characteristics of the overnight passenger flows for the hotel accommodation and significantly improve the forecasting performance of the model. Forecasting results can provide necessary references for decision-making in tourism-related industries, and this forecasting framework can also be extended to other similar complex time series forecasting problems.
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.
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