A New Secondary Decomposition–Ensemble Approach for Tourism Demand Forecasting

Decomposition methods are widely used to process the nonlinear and nonstationary characteristics of tourism‐demand time series by decomposing the original data into different components with different timescales. However, the new issue is that some components after the first decomposition are still unstable and irregular, and there remains limited evidence on how to further process these unstable and irregular components effectively. Therefore, in this study, a novel secondary decomposition–ensemble approach, including empirical mode decomposition (EMD) and variational mode decomposition (VMD), is first proposed to forecast tourism demand. Correspondingly, the point and interval forecasting results show that the proposed approach generally improves forecasting performance relative to the selected benchmark models, particularly in the normal‐period and political‐unrest datasets. The COVID‐period results are more mixed, suggesting that the proposed framework remains competitive under extreme disruption but does not uniformly dominate all one‐stage decomposition alternatives across all markets and horizons.