A decomposition-ensemble approach for tourism forecasting
Xie, Gang1; Qian, Yatong1,2; Wang, Shouyang1
AbstractWith 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.
KeywordTourism demand Complete ensemble empirical mode decomposition with adaptive noise Data characteristic analysis Time series forecasting
Indexed BySCI
Funding ProjectNational Natural Science Foundation of China[71771207] ; National Natural Science Foundation of China[71642006] ; National Center for Mathematics and Interdisciplinary Sciences, CAS
WOS Research AreaSocial Sciences - Other Topics ; Sociology
WOS SubjectHospitality, Leisure, Sport & Tourism ; Sociology
WOS IDWOS:000527848700029
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Document Type期刊论文
Corresponding AuthorXie, Gang
Affiliation1.Chinese Acad Sci, Acad Math & Syst Sci, MDIS, CFS, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
Recommended Citation
GB/T 7714
Xie, Gang,Qian, Yatong,Wang, Shouyang. A decomposition-ensemble approach for tourism forecasting[J]. ANNALS OF TOURISM RESEARCH,2020,81:16.
APA Xie, Gang,Qian, Yatong,&Wang, Shouyang.(2020).A decomposition-ensemble approach for tourism forecasting.ANNALS OF TOURISM RESEARCH,81,16.
MLA Xie, Gang,et al."A decomposition-ensemble approach for tourism forecasting".ANNALS OF TOURISM RESEARCH 81(2020):16.
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