Empirically Assessing the Predictivity of Tourism Demand Data

Accurate forecasting of tourism demand is critical for policy and business planning yet remains challenging due to the inherent complexity and vulnerability of tourism demand to external shocks. This study introduces a novel predictivity metric based on Weighted Permutation Entropy (WPE) for assessing the intrinsic predictivity of tourism demand data. Building on the limitations of existing entropy measures, particularly Sample Entropy (SampEn) and Multiscale SampEn, WPE is proposed for its effectiveness in capturing both ordinal and amplitude dynamics of the tourism demand, especially under external shocks, such as the COVID-19 pandemic. Using monthly tourist arrival data from Australia, the study evaluates the predictivity of tourism demand across different temporal scales and lengths. The study provides actionable insights for enhancing tourism demand forecasting by optimizing data aggregation scales and adapting the predictivity metric during volatile periods.