A decomposition-ensemble approach for tourism forecasting

Gang Xie; Yatong Qian; Shouyang Wang

doi:10.1016/j.annals.2020.102891

A decomposition-ensemble approach for tourism forecasting

Ann Tour Res. 2020 Mar:81:102891. doi: 10.1016/j.annals.2020.102891. Epub 2020 Feb 25.

Authors

Gang Xie¹, Yatong Qian^{1

2}, Shouyang Wang¹

Affiliations

¹ CFS, MDIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China.
² University of Chinese Academy of Sciences, Beijing 100049, China.

Abstract

With the frequent occurrence of irregular events in recent years, the tourism industry in some areas, such as Hong Kong, has suffered great volatility. To enhance the predictive accuracy of tourism demand forecasting, a decomposition-ensemble approach is developed based on the complete ensemble empirical mode decomposition with adaptive noise, data characteristic analysis, and the Elman's neural network model. Using Hong Kong tourism demand as an empirical case, this study firstly investigates how data characteristic analysis is used in a decomposition-ensemble approach. The empirical results show that the proposed model outperforms other models in both point and interval forecasts for different prediction horizons, indicating the effectiveness of the proposed approach for forecasting tourism demand, especially for time series with complexity.

Keywords: Complete ensemble empirical mode decomposition with adaptive noise; Data characteristic analysis; Time series forecasting; Tourism demand.