Fine-grained tourism demand forecasting: A decomposition ensemble deep learning model

Jian-Wu Bi, Tian-Yu Han and Yanbo Yao

Tourism Economics, 2023, vol. 29, issue 7, 1736-1763

Abstract: Compared with coarse-grained forecasting, fine-grained tourism demand forecasting is a more challenging task, but research on this issue is very scarce. To address this issue, a decomposition ensemble deep learning model is proposed by integrating CEEMDAN, CNNs, LSTM networks, and AR models. The CEEMDAN can decompose complex tourism demand data into multiple components with simpler characteristics, thereby reducing the complexity of forecasting. The CNNs and LSTM networks can fully capture the locally recurring patterns and the long-term dependencies of the components obtained by CEEMDAN. The AR model can capture the scale of tourism demand data, which can overcome the problem that the output scale of the deep neural networks (i.e., CNNs and LSTM networks) is not sensitive to the scale of the inputs. The effectiveness of the proposed model is verified by comparing with five benchmark models using real-time data on tourist volumes at two attractions.

Keywords: fine-grained tourism demand forecasting; deep learning; data decomposition; ensemble learning (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/13548166231158705 (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:29:y:2023:i:7:p:1736-1763

DOI: 10.1177/13548166231158705

Access Statistics for this article

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