Tourism demand forecasting: An ensemble deep learning approach
Sun, Shaolong1; Li, Yanzhao1; Guo, Ju-e1; Wang, Shouyang2,3,4
AbstractThe availability of tourism-related big data increases the potential to improve the accuracy of tourism demand forecasting but presents significant challenges for forecasting, including curse of dimensionality and high model complexity. A novel bagging-based multivariate ensemble deep learning approach integrating stacked autoencoder and kernel-based extreme learning machine (B-SAKE) is proposed to address these challenges in this study. By using historical tourist arrival data, economic variable data, and search intensity index (SII) data, we forecast tourist arrivals in Beijing from four countries. The consistent results of multiple schemes suggest that our proposed B-SAKE approach outperforms the benchmark models in terms of level accuracy, directional accuracy, and even statistical significance. Both bagging and stacked autoencoder can effectively alleviate the challenges brought by tourism big data and improve the forecasting performance of the models. The ensemble deep learning model we propose contributes to tourism demand forecasting literature and benefits relevant government officials and tourism practitioners.
Keywordbagging economic variables ensemble deep learning search intensity index stacked autoencoder tourism demand forecasting
Indexed BySCI
Funding ProjectNational Natural Science Foundation of China[71988101] ; National Natural Science Foundation of China[71642006] ; Fundamental Research Funds for the Central Universities[SK2021007]
WOS Research AreaBusiness & Economics ; Social Sciences - Other Topics
WOS SubjectEconomics ; Hospitality, Leisure, Sport & Tourism
WOS IDWOS:000671417000001
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Document Type期刊论文
Corresponding AuthorSun, Shaolong
Affiliation1.Xi An Jiao Tong Univ, Sch Management, 28 Xianning West Rd, Xian 710049, Peoples R China
2.Chinese Acad Sci, Acad Math & Syst Sci, Management Sci, Beijing, Peoples R China
3.Univ Chinese Acad Sci, Management Sci & Engn, Beijing, Peoples R China
4.Chinese Acad Sci, Ctr Forecasting Sci, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Sun, Shaolong,Li, Yanzhao,Guo, Ju-e,et al. Tourism demand forecasting: An ensemble deep learning approach[J]. TOURISM ECONOMICS,2021:29.
APA Sun, Shaolong,Li, Yanzhao,Guo, Ju-e,&Wang, Shouyang.(2021).Tourism demand forecasting: An ensemble deep learning approach.TOURISM ECONOMICS,29.
MLA Sun, Shaolong,et al."Tourism demand forecasting: An ensemble deep learning approach".TOURISM ECONOMICS (2021):29.
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