Yishuo, ZhangGang, LiMuskat, BirgitVu, Quan HuyLaw, Rob2021-11-050160-7383http://hdl.handle.net/1885/251610As tourism researchers continue to search for solutions to determine the best possible forecasting performance, it is important to understand the maximum predictivity achieved by models, as well as how various data characteristics influence the maximum predictivity. Drawing on information theory, the predictivity of tourism demand data is quantitatively evaluated and beneficial for improving the performance of tourism demand forecasting. Empirical results from Hong Kong tourism demand data show that 1) the predictivity could largely help the researchers estimate the best possible forecasting performance and understand the influence of various data characteristics on the forecasting performance.; 2) the predictivity can be used to assess the short effect of external shock — such as SARS over tourism demand forecasting.The work was completed when Gang Li was on ASL in Chinese Academy of Sciences in 2019, and we would like to thank Deakin University's ASL2019 fund and Xinjiang Science & Technology Research fund with Chinese Academy of Sciences.application/pdfen-AU© 2021 Elsevier Ltdhttps://creativecommons.org/licenses/by-nc-nd/4.0/Data characteristicsEntropyPredictivityTourism demand forecasting2021-07CC BY-NC-ND