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Seasonal and trend forecasting of tourist arrivals: An adaptive multiscale ensemble learning approach
Xing, Guangyuan1; Sun, Shaolong2; Bi, Dan2; Guo, Ju-e2; Wang, Shouyang3,4,5
2022-01-28
Source PublicationINTERNATIONAL JOURNAL OF TOURISM RESEARCH
ISSN1099-2340
Pages18
AbstractIn this study, we propose an adaptive multiscale ensemble (AME) learning approach, which consists of variational mode decomposition (VMD) and least square support vector regression (LSSVR) for seasonal and trend forecasting of tourist arrivals. In the formulation of AME learning approach, the original tourist arrival series is decomposed into the trend, seasonal, and remaining volatility components. Then, ARIMA is used to forecast the trend component, SARIMA is used to forecast the seasonal component, and LSSVR is used to forecast the remaining volatility components. The empirical results demonstrate that our proposed AME learning approach can achieve higher forecasting accuracy.
Keywordensemble learning least square support vector regression seasonality tourism demand forecasting variational mode decomposition
DOI10.1002/jtr.2512
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[72101197] ; National Natural Science Foundation of China[71988101] ; Fundamental Research Funds for the Central Universities[SK2021007]
WOS Research AreaSocial Sciences - Other Topics
WOS SubjectHospitality, Leisure, Sport & Tourism
WOS IDWOS:000748176700001
PublisherWILEY
Citation statistics
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/59918
Collection中国科学院数学与系统科学研究院
Corresponding AuthorSun, Shaolong
Affiliation1.Xi An Jiao Tong Univ, Sch Econ & Finance, Xian, Peoples R China
2.Xi An Jiao Tong Univ, Sch Management, Xian 710049, Peoples R China
3.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China
4.Univ Chinese Acad Sci, Sch Econ & Management, Beijing, Peoples R China
5.Chinese Acad Sci, Ctr Forecasting Sci, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Xing, Guangyuan,Sun, Shaolong,Bi, Dan,et al. Seasonal and trend forecasting of tourist arrivals: An adaptive multiscale ensemble learning approach[J]. INTERNATIONAL JOURNAL OF TOURISM RESEARCH,2022:18.
APA Xing, Guangyuan,Sun, Shaolong,Bi, Dan,Guo, Ju-e,&Wang, Shouyang.(2022).Seasonal and trend forecasting of tourist arrivals: An adaptive multiscale ensemble learning approach.INTERNATIONAL JOURNAL OF TOURISM RESEARCH,18.
MLA Xing, Guangyuan,et al."Seasonal and trend forecasting of tourist arrivals: An adaptive multiscale ensemble learning approach".INTERNATIONAL JOURNAL OF TOURISM RESEARCH (2022):18.
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