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A new decomposition ensemble approach for tourism demand forecasting: Evidence from major source countries in Asia‐Pacific region
Author(s) -
Zhang Chengyuan,
Jiang Fuxin,
Wang Shouyang,
Sun Shaolong
Publication year - 2021
Publication title -
international journal of tourism research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.155
H-Index - 58
eISSN - 1522-1970
pISSN - 1099-2340
DOI - 10.1002/jtr.2445
Subject(s) - tourism , decomposition , destinations , multivariate statistics , hilbert–huang transform , econometrics , asia pacific , ensemble learning , empirical research , demand forecasting , computer science , economics , business , geography , marketing , economy , statistics , artificial intelligence , mathematics , machine learning , telecommunications , ecology , archaeology , white noise , biology
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.