Tourism demand forecasting under conceptual drift during COVID-19: an ensemble deep learning model
Jian-Wu Bi,
Tian-Yu Han,
Yanbo Yao and
Tao Yang
Current Issues in Tourism, 2024, vol. 27, issue 23, 4084-4103
Abstract:
To address the issue of tourism demand forecasting in the context of concept drift, a new ensemble deep learning model based on transformer is proposed, which includes three parts: data processing, base predictor pool construction, base predictor selection and combination. In the first part, the relevant data are collected and converted into the input form required by transformer. In the second part, a base predictor pool containing multiple predictors is constructed, where each predictor can capture a specific concept from historical data. In the final part, a predictor selection algorithm is proposed to select ‘effective predictors’ from the base predictor pool. These effective predictors are further integrated to generate the final forecasts. The proposed model is applied to the forecast of tourist volume of two attractions in China. The results show that the proposed model outperforms the benchmark models in the context of concept drift, benchmarked against eight models.
Date: 2024
References: Add references at CitEc
Citations:
Downloads: (external link)
http://hdl.handle.net/10.1080/13683500.2023.2273922 (text/html)
Access to full text is restricted to subscribers.
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:taf:rcitxx:v:27:y:2024:i:23:p:4084-4103
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/rcit20
DOI: 10.1080/13683500.2023.2273922
Access Statistics for this article
Current Issues in Tourism is currently edited by Jennifer Tunstall
More articles in Current Issues in Tourism from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().