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Forecasting tourism demand with multisource big data

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  • Li, Hengyun
  • Hu, Mingming
  • Li, Gang

Abstract

Based on internet big data from multiple sources (i.e., the Baidu search engine and two online review platforms, Ctrip and Qunar), this study forecasts tourist arrivals to Mount Siguniang, China. Key findings of this empirical study indicate that (a) tourism demand forecasting based on internet big data from a search engine and online review platforms can significantly improve forecasting performance; (b) compared with tourism demand forecasting based on single-source data from a search engine, demand forecasting based on multisource big data from a search engine and online review platforms demonstrates better performance; and (c) compared with tourism demand forecasting based on online review data from a single platform, forecasting performance based on multiple platforms is significantly better.

Suggested Citation

  • Li, Hengyun & Hu, Mingming & Li, Gang, 2020. "Forecasting tourism demand with multisource big data," Annals of Tourism Research, Elsevier, vol. 83(C).
    • Handle: RePEc:eee:anture:v:83:y:2020:i:c:s0160738320300566
    DOI: 10.1016/j.annals.2020.102912
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    8. Bulatovic, Iva & Papatheodorou, Andreas, 2023. "Civil aviation and tourism demand in Montenegro: A panel data approach," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 9(1), pages 25-36.
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    20. Weaver, Adam, 2021. "Tourism, big data, and a crisis of analysis," Annals of Tourism Research, Elsevier, vol. 88(C).
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    22. Zhang, Yishuo & Li, Gang & Muskat, Birgit & Vu, Huy Quan & Law, Rob, 2021. "Predictivity of tourism demand data," Annals of Tourism Research, Elsevier, vol. 89(C).
    23. Jian-Wu Bi & Tian-Yu Han & Yanbo Yao, 2024. "Collaborative forecasting of tourism demand for multiple tourist attractions with spatial dependence: A combined deep learning model," Tourism Economics, , vol. 30(2), pages 361-388, March.
    24. Jessie Bravo & Roger Alarcón & Carlos Valdivia & Oscar Serquén, 2023. "Application of Machine Learning Techniques to Predict Visitors to the Tourist Attractions of the Moche Route in Peru," Sustainability, MDPI, vol. 15(11), pages 1-25, June.

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