A novel two-stage combination model for tourism demand forecasting

Abstract

The tourism literature has shown that a combination of tourism forecasting models can provide better performance than individual models. In the literature, the models to be combined are usually subjectively selected and authors focus on the combination method. In this study, a new direction is represented by the integration of forecasts from several models using a two-stage forecasting system. In stage I, a subset of the best available models is objectively selected according to their performance. Then, in stage II, linear and nonlinear combination methods are utilized to integrate the forecast values of the optimal subset. The empirical study using tourist arrivals in Hong Kong from five major source markets indicates that the proposed two-stage tourism forecasting system with linear combination substantially improves forecasting performance compared with benchmarks. This study also finds that when combining forecasts from different models, the linear combination method is more suitable than nonlinear AI models.