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Big Data analytics for forecasting tourism destination arrivals with the applied Vector Autoregression model

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  • Liu, Yuan-Yuan
  • Tseng, Fang-Mei
  • Tseng, Yi-Heng

Abstract

The prediction of tourist numbers is important for Destination Management and Marketing. While most existing methods rely on well-structured statistical data, using web search queries of the destination to forecast its tourist arrivals is a new way to apply Big Data analytics. However, there are no studies exploring correlation of weather, temperatures, weekends and public holidays with tourism destination arrivals and web search queries of the destination, respectively. This study uses the Vector Autoregressive modeling to examine the Granger causality between actual arrivals of the studied cultural tourism destination and its web search queries, and to explore the correlation mentioned above. The striking result is that weather has no correlation either with actual arrivals of the studied cultural tourism destination, or with its web search queries. Meanwhile, unlike previous researchers who discuss the predictive power of web queries on actual tourism flows, this study emphasizes their reciprocal predictive powers upon each other. The originality of this study is exemplifying the utilization of Big Data analytics in the tourism domain with Big Data datasets, data capture techniques, analytical tools, and analysis results. This study further digs possible reasons for an identified short time lag length (p = 2), to provide insights for Destination Management and Marketing.

Suggested Citation

  • Liu, Yuan-Yuan & Tseng, Fang-Mei & Tseng, Yi-Heng, 2018. "Big Data analytics for forecasting tourism destination arrivals with the applied Vector Autoregression model," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 123-134.
    • Handle: RePEc:eee:tefoso:v:130:y:2018:i:c:p:123-134
    DOI: 10.1016/j.techfore.2018.01.018
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    3. Ilsé Botha & Andrea Saayman, 2024. "Does Google Analytics Improve the Prediction of Tourism Demand Recovery?," Forecasting, MDPI, vol. 6(4), pages 1-17, October.
    4. Thomas J. Lampoltshammer & Stefanie Wallinger & Johannes Scholz, 2023. "Bridging Disciplinary Divides through Computational Social Sciences and Transdisciplinarity in Tourism Education in Higher Educational Institutions: An Austrian Case Study," Sustainability, MDPI, vol. 15(10), pages 1-16, May.
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    7. Ziqi Yuan & Guozhu Jia, 2022. "Systematic investigation of keywords selection and processing strategy on search engine forecasting: a case of tourist volume in Beijing," Information Technology & Tourism, Springer, vol. 24(4), pages 547-580, December.
    8. Wu, Lunwen & Wang, Zhouyiying & Liao, Zhixue & Xiao, Di & Han, Peng & Li, Wenyong & Chen, Qin, 2024. "Multi-day tourism recommendations for urban tourists considering hotel selection: A heuristic optimization approach," Omega, Elsevier, vol. 126(C).
    9. Li, Cheng & Ge, Peng & Liu, Zhusheng & Zheng, Weimin, 2020. "Forecasting tourist arrivals using denoising and potential factors," Annals of Tourism Research, Elsevier, vol. 83(C).
    10. Jianxin Zhang & Yuting Yan & Jinyue Zhang & Peixue Liu & Li Ma, 2023. "Investigating the Spatial-Temporal Variation of Pre-Trip Searching in an Urban Agglomeration," Sustainability, MDPI, vol. 15(14), pages 1-17, July.
    11. Silva, Emmanuel Sirimal & Hassani, Hossein & Heravi, Saeed & Huang, Xu, 2019. "Forecasting tourism demand with denoised neural networks," Annals of Tourism Research, Elsevier, vol. 74(C), pages 134-154.
    12. Alemayehu, Fikru K. & Kumbhakar, Subal C. & Landazuri Tveteraas, Sigbjørn, 2022. "Estimation of staff use efficiency: Evidence from the hospitality industry," Technological Forecasting and Social Change, Elsevier, vol. 178(C).
    13. Song, Haiyan & Qiu, Richard T.R. & Park, Jinah, 2019. "A review of research on tourism demand forecasting," Annals of Tourism Research, Elsevier, vol. 75(C), pages 338-362.
    14. Ulrich Gunter & Irem Önder & Stefan Gindl, 2019. "Exploring the predictive ability of LIKES of posts on the Facebook pages of four major city DMOs in Austria," Tourism Economics, , vol. 25(3), pages 375-401, May.
    15. Zhang, Yishuo & Li, Gang & Muskat, Birgit & Law, Rob & Yang, Yating, 2020. "Group pooling for deep tourism demand forecasting," Annals of Tourism Research, Elsevier, vol. 82(C).
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    17. Shaolong Suna & Dan Bi & Ju-e Guo & Shouyang Wang, 2020. "Seasonal and Trend Forecasting of Tourist Arrivals: An Adaptive Multiscale Ensemble Learning Approach," Papers 2002.08021, arXiv.org, revised Mar 2020.
    18. Guizzardi, Andrea & Pons, Flavio Maria Emanuele & Angelini, Giovanni & Ranieri, Ercolino, 2021. "Big data from dynamic pricing: A smart approach to tourism demand forecasting," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1049-1060.

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