文献类型：期刊文献

英文题名：Forecasting Tourism Demand with Decomposed Search Cycles

作者：Li, Xin[1];Law, Rob[2]

通讯作者：Li, X[1]

机构：[1]Univ Sci & Technol Beijing, Donlinks Sch Econ & Management, Beijing, Peoples R China;[2]Hong Kong Polytech Univ, Sch Hotel & Tourism Management, Kowloon, Hong Kong, Peoples R China

第一机构：Univ Sci & Technol Beijing, Donlinks Sch Econ & Management, Beijing, Peoples R China

通讯机构：[1]corresponding author), Beijing Union Univ, Inst Tourism, Collaborat Innovat Ctr eTourism, Beijing 100101, Peoples R China.|[1141732]北京联合大学旅游学院;[11417]北京联合大学;

年份：2020

卷号：59

期号：1

起止页码：52-68

外文期刊名：JOURNAL OF TRAVEL RESEARCH

收录：;Scopus(收录号：2-s2.0-85060949278);WOS:【SSCI(收录号:WOS:000508454100004)】；

基金：The author(s) received the following financial support for the research, authorship, and/or publication of this article: The study was partly supported by a research grant from the National Natural Science Foundation of China (No. 71601021).

语种：英文

外文关键词：Google search data; ensemble empirical mode decomposition; tourism demand; tourism forecasting

摘要：This study aims to examine whether decomposed search engine data can be used to improve the forecasting accuracy of tourism demand. The methodology was applied to predict monthly tourist arrivals from nine countries to Hong Kong. Search engine data from Google Trends were first decomposed into different components using an ensemble empirical mode decomposition method and then the cyclical components were examined through statistical analysis. Forecasting models with rolling window estimation were implemented to predict the tourist arrivals to Hong Kong. Results indicate the proposed methodology can outperform the benchmark model in the out-of-sample forecasting evaluation of Choi and Varian (2012). The findings also demonstrate that our proposed methodology is superior in forecasting turning points. This study proposes a unique decomposition-based perspective on tourism forecasting using online search engine data.