Tourism demand forecasting: An ensemble deep learning approach

Shaolong Sun, Yanzhao Li, Ju-e Guo and Shouyang Wang
Additional contact information
Shaolong Sun: Xi’an Jiaotong University, China
Yanzhao Li: Xi’an Jiaotong University, China
Ju-e Guo: Xi’an Jiaotong University, China
Shouyang Wang: Academy of Mathematics and Systems Science, Chinese Academy of Sciences, China

Tourism Economics, 2022, vol. 28, issue 8, 2021-2049

Abstract: The 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.

Keywords: bagging; economic variables; ensemble deep learning; search intensity index; stacked autoencoder; tourism demand forecasting (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/13548166211025160 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:toueco:v:28:y:2022:i:8:p:2021-2049

DOI: 10.1177/13548166211025160

Access Statistics for this article

More articles in Tourism Economics
Bibliographic data for series maintained by SAGE Publications ().