Hengyun Li , Honggang Guo , Jianzhou Wang , Yong Wang , Chunying Wu
{"title":"Tourism combination forecasting with swarm intelligence","authors":"Hengyun Li , Honggang Guo , Jianzhou Wang , Yong Wang , Chunying Wu","doi":"10.1016/j.annals.2025.103932","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":48452,"journal":{"name":"Annals of Tourism Research","volume":"111 ","pages":"Article 103932"},"PeriodicalIF":7.8000,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of Tourism Research","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0160738325000386","RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/2/26 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"HOSPITALITY, LEISURE, SPORT & TOURISM","Score":null,"Total":0}
引用次数: 0
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.
期刊介绍：
The Annals of Tourism Research is a scholarly journal that focuses on academic perspectives related to tourism. The journal defines tourism as a global economic activity that involves travel behavior, management and marketing activities of service industries catering to consumer demand, the effects of tourism on communities, and policy and governance at local, national, and international levels. While the journal aims to strike a balance between theory and application, its primary focus is on developing theoretical constructs that bridge the gap between business and the social and behavioral sciences. The disciplinary areas covered in the journal include, but are not limited to, service industries management, marketing science, consumer marketing, decision-making and behavior, business ethics, economics and forecasting, environment, geography and development, education and knowledge development, political science and administration, consumer-focused psychology, and anthropology and sociology.
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