Modeling And Forecasting Tourism Demand Of Hong Kong With Baidu Index

Tourism demand forecasting plays a crucial role for tourism practitioners in investment decisions and industrial policymaking.In the current era of the rapid development of network information,potential tourists usually use the network to obtain local relevant information before traveling,and use this to reasonably arrange itineraries.This search behavior information facilitates researchers 'understanding of tourists' potential needs and can further improve tourism demand forecasts.In addition,some studies have shown that search query data(such as the Baidu Index),as a new data source,has a considerable improvement over traditional data and can predict travel demand well.However,due to many variables in the Internet search data,how to choose a suitable model to use this data to predict tourism demand is worth exploring.Therefore,this paper introduces two types of methods that can use Baidu index to predict tourism demand: machine learning technology and traditional factor models,and applies them to the forecast of Hong Kong's tourism demand in the mainland,and conducts comparative studies.First,this article explores and determines the six factors of tourism as predictive indicators,and then extracts the Baidu Index related to the number of mainland visitors to Hong Kong and the six factors of tourism.Second,the prediction model is divided into two categories: random forest and factor auto regression model.The factor auto regression model also includes two methods,namely the generalized dynamic factor model and the principal component analysis method,which are used to predict the slow-growth and fast-growth mainland's tourism demand for Hong Kong.Finally,the prediction effects of different models at different stages are evaluated based on the directional accuracy test,DM test,and model confidence set test.The empirical results in this paper show that:(1)Compared with the factor analysis model,the random forest has a significant advantage in the inflection point prediction of seasonal trend transition in a stable prediction interval,and can grasp the trend switching faster.The research on tourism demand forecasting shows a good directional forecasting results.(2)When tourism demand is at a rapid growth stage,the generalized dynamic factor model is more advantageous in terms of information extraction.(3)Under the same sample size and tourism demand stage,the static factors obtained by principal component analysis did not show significant advantages.They were inferior to the random forest algorithm in predicting trends and slightly worse than the generalized dynamic factors in the prediction accuracy level model.(4)Due to the intricate relationship between the factors affecting tourism demand,the random forest model is more suitable for prediction if the industry situation is unknown and the future trend is unclear.On the one hand,accurately forecasting tourism demand can ensure the efficient allocation of resources and the safety of high-quality services,and help adjust the supply of related products or services on time to avoid imbalances between supply and demand.On the other hand,the information such as the elasticity of tourism demand obtained in the forecast of tourism demand helps the managers and investors of the tourism industry to respond quickly,arrange schedules and staffing in time,and write tourism brochures,which diversify the tourism products.In a word,due to the numerous and complicated factors affecting tourism demand,accurately predicting tourism demand is a meaningful and challenging study.