A new decomposition ensemble approach for tourism demand forecasting: Evidence from major source countries in Asia-Pacific region

Previous studies have shown that different market factors influence tourism demand at different timescales. Accordingly, we propose the decomposition ensemble learning approach to analyze impact of different market factors on tourism demand, and explore the potential advantages of the proposed method on forecasting tourism demand in Asia-Pacific region. By decomposing tourist arrivals with noise-assisted multivariate empirical mode decomposition, this study further explores the multiscale relationship between tourist destinations and major source countries. The empirical results show that decomposition ensemble approach performs significantly better than benchmarks in terms of the level forecasting accuracy and directional forecasting accuracy.