Tourism combination forecasting with swarm intelligence

Hengyun Li, Honggang Guo, Jianzhou Wang, Yong Wang and Chunying Wu

Annals of Tourism Research, 2025, vol. 111, issue C

Abstract: Combination forecasting is an effective method for improving the accuracy of tourism demand. This study proposes an innovative combination strategy based on a multi-objective swarm intelligence optimization algorithm and, for the first time, examines whether and how this algorithm can enhance the performance of tourism demand combination forecasting. An empirical study conducted under several scenarios demonstrates that the proposed combination strategy enhances the interaction among single forecasts, leading to improved forecast accuracy and stability compared with traditional combination methods. The model remained effective even during the COVID-19 pandemic. The findings have a positive impact on predictive research, offering new insights and methodologies for tourism demand modeling.

Keywords: Tourism demand forecasting; Combination forecasts; Swarm intelligence optimization algorithm; Multi-objective optimization (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0160738325000386
Full text for ScienceDirect subscribers only

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:eee:anture:v:111:y:2025:i:c:s0160738325000386

DOI: 10.1016/j.annals.2025.103932

Access Statistics for this article

Annals of Tourism Research is currently edited by John Tribe

More articles in Annals of Tourism Research from Elsevier
Bibliographic data for series maintained by Catherine Liu ().