Timely predicting tourist demand is extremely important for the tourism industry. However, due to limited availability of data, most of the relevant research studies have focused on data on a quarterly or monthly basis. In this article, we propose a novel hybrid approach, SARIMA + LSTM, that is, seasonal autoregressive integrated moving average (SARIMA) combined with long short-term memory (LSTM) to forecast daily tourist arrivals to Macau SAR, China. The LSTM model is a novel artificial intelligence nonlinear method which has been shown to have the capacity to learn the long-term dependencies existing in the time series. SARIMA + LSTM benefits from the predictive power of the SARIMA model and the ability of the LSTM to further reduce residuals. The results show that the SARIMA + LSTM forecast technique outperforms other methods.

The purpose of this study was to examine if there were differences in perception by guests on safety and security of Airbnb and licensed properties. The results indicate there are differences in perception on the safety and security by guests of Airbnb and licensed properties. The implication of the study is that Airbnb properties should consider improving their safety and security measures from the perspectives of both service quality and marketing.