Tourism combination forecasting with swarm intelligence
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 pe...
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| Vydáno v: | Annals of tourism research Ročník 111; s. 103932 |
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| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier Ltd
01.03.2025
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| Témata: | |
| ISSN: | 0160-7383 |
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| 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.
•This study forecasts daily and weekly tourism demand for three tourism destinations.•A novel combination method based on multi-objective swarm intelligence is proposed.•The proposed method can enhance both forecast accuracy and stability.•The proposed method can improve forecast accuracy even in turbulent period. |
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| AbstractList | 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.
•This study forecasts daily and weekly tourism demand for three tourism destinations.•A novel combination method based on multi-objective swarm intelligence is proposed.•The proposed method can enhance both forecast accuracy and stability.•The proposed method can improve forecast accuracy even in turbulent period. |
| ArticleNumber | 103932 |
| Author | Wang, Jianzhou Li, Hengyun Wang, Yong Guo, Honggang Wu, Chunying |
| Author_xml | – sequence: 1 givenname: Hengyun surname: Li fullname: Li, Hengyun email: neilhengyun.li@polyu.edu.hk organization: School of Hotel and Tourism Management, The Hong Kong Polytechnic University, Hong Kong – sequence: 2 givenname: Honggang surname: Guo fullname: Guo, Honggang email: ghg970612@163.com organization: School of Data Science and Artificial Intelligence, Dongbei University of Finance and Economics, Dalian, China – sequence: 3 givenname: Jianzhou surname: Wang fullname: Wang, Jianzhou email: wangjz@dufe.edu.cn organization: School of Statistics, Dongbei University of Finance and Economics, Dalian, China – sequence: 4 givenname: Yong surname: Wang fullname: Wang, Yong email: ywang@dufe.edu.cn organization: School of Statistics, Dongbei University of Finance and Economics, Dalian, China – sequence: 5 givenname: Chunying surname: Wu fullname: Wu, Chunying email: wcydufe@163.com organization: School of Management Science and Engineering, Shandong University of Finance and Economics, Shandong, China |
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| Cites_doi | 10.1016/j.annals.2020.102923 10.1057/jors.1969.103 10.1016/j.annals.2020.102912 10.1177/0047287520934871 10.1016/j.annals.2021.103149 10.1016/j.ins.2022.09.056 10.1016/j.annals.2018.12.001 10.1016/j.annals.2010.05.003 10.1002/jtr.1953 10.1016/j.tourman.2024.105004 10.1016/S0925-2312(01)00702-0 10.1016/j.tourman.2022.104490 10.1007/s00521-015-1920-1 10.3389/fenrg.2023.1078751 10.1016/j.annals.2019.01.010 10.1108/IJCHM-05-2015-0249 10.1016/j.rser.2014.12.012 10.1109/JAS.2021.1004129 10.1016/j.ijhm.2024.103750 10.1016/j.annals.2023.103667 10.1016/j.annals.2023.103675 10.1016/j.advengsoft.2013.12.007 10.1016/j.tourman.2006.08.003 10.1016/j.annals.2024.103871 10.1016/j.annals.2024.103791 10.1016/j.annals.2021.103155 10.1007/s00366-022-01604-x 10.1016/j.annals.2021.103197 10.1016/j.annals.2021.103271 10.1177/00472875211040569 10.1016/j.annals.2020.103117 10.1016/j.annals.2019.01.014 10.1016/j.annals.2022.103402 10.1016/j.annals.2020.102943 10.1016/j.annals.2021.103258 10.1016/j.tourman.2014.07.019 |
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| Keywords | Multi-objective optimization Combination forecasts Swarm intelligence optimization algorithm Tourism demand forecasting |
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