Forecasting Hotel Accommodation Demand Based on LSTM Model Incorporating Internet Search Index

Abstract

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.