NÂNG CAO HIỆU QUẢ DỰ BÁO NHU CẦU DU LỊCH VỚI GOOGLE TRENDS
hue-hujos-tt-7658.pdf · Phiên bản 1 · 16 trang
Ngô Văn Sơn và CS. Tập 133, Số 2A, 2024 98 3. M. Fathi, M. Haghi Kashani, S. M. Jameii, and E. Mahdipour, “Big Data Analytics in Weather Forecasting: A Systematic Review,” Arch. Comput. Methods Eng., vol. 29, no. 2, pp. 1247–1275, 2022, doi: 10.1007/s11831-021-09616-4. 4. H. Li, M. Hu, and G. Li, “Forecasting tourism demand with multisource big data,” Ann. Tour. Res., vol. 83, no. January, p. 102912, 2020, doi: 10.1016/j.annals.2020.102912. 5. S. P. Jun, H. S. Yoo, and S. Choi, “Ten years of research change using Google Trends: From the perspective of big data utilizations and applications,” Technol. Forecast. Soc. Change, vol. 130, no. February 2017, pp. 69–87, 2018, doi: 10.1016/j.techfore.2017.11.009. 6. Y. Li, Z. Lin, and S. Xiao, “Using social media big data for tourist demand forecasting: A new machine learning analytical approach,” J. Digit. Econ., vol. 1, no. 1, pp. 32–43, 2022, doi: 10.1016/j.jdec.2022.08.006. 7. M. Á. R. Reina, “Big Data: Forecasting and Control for Tourism Demand,” no. January 2018, pp. 273– 286, 2020, doi: 10.1007/978-3-030-56219-9_18. 8. “Số liệu thống kê Khách quốc tế.” https://vietnamtourism.gov.vn/statistic/international (accessed Aug. 15, 2024). 9. “Overseas travel and tourism time series - Office for National Statistics.” https://www.ons.gov.uk/peoplepopulationandcommunity/leisureandtourism/datasets/internationalpa ssengersurveytimeseriesspreadsheet (accessed Aug. 21, 2024). 10. Y. Carrière-Swallow and F. Labbé, “Nowcasting with Google trends in an emerging market,” J. Forecast., vol. 32, no. 4, pp. 289–298, 2013, doi: 10.1002/for.1252.
11. C. Lim and M. McAleer, “Forecasting tourist arrivals,” Ann. Tour. Res., vol. 28, no. 4, pp. 965–977, 2001, doi: 10.1016/S0160-7383(01)00006-8. 12. V. Cho, “A comparison of three different approaches to tourist arrival forecasting,” Tour. Manag., vol. 24, no. 3, pp. 323–330, 2003, doi: 10.1016/S0261-5177(02)00068-7. 13. H. Hassani, E. S. Silva, N. Antonakakis, G. Filis, and R. Gupta, “Forecasting accuracy evaluation of tourist arrivals,” Ann. Tour. Res., vol. 63, pp. 112–127, 2017, doi: 10.1016/j.annals.2017.01.008. 14. M. E. Nor, A. I. M. Nurul, and M. S. Rusiman, “A Hybrid Approach on Tourism Demand Forecasting,” J. Phys. Conf. Ser., vol. 995, no. 1, pp. 1–11, Apr. 2018, doi: 10.1088/1742-6596/995/1/012034. 15. M. L. Shen, H. H. Liu, Y. H. Lien, C. F. Lee, and C. H. Yang, “Hybrid approach for forecasting tourist arrivals,” ACM Int. Conf. Proceeding Ser., vol. Part F1479, pp. 392–396, 2019, doi: 10.1145/3316615.3316628. 16. Purwanto, Sunardi, F. T. Julfia, and A. Paramananda, “Hybrid model of ARIMA-linear trend model for tourist arrivals prediction model in Surakarta City, Indonesia,” AIP Conf. Proc., vol. 2114, no. June, 2019, doi: 10.1063/1.5112481. 17. K. Volchek, A. Liu, H. Song, and D. Buhalis, “Forecasting tourist arrivals at attractions: Search engine empowered methodologies,” Tour. Econ., vol. 25, no. 3, pp. 425–447, 2019, doi: 10.1177/1354816618811558. 18. K. H. Huarng and T. H. K. Yu, “Application of Google trends to forecast tourism demand,” J. Internet Technol., vol. 20, no. 4, pp. 1273–1280, 2019, doi: 10.3966/160792642019072004025.
19. T. Havranek and A. Zeynalov, “Forecasting tourist arrivals: Google Trends meets mixed-frequency data,” Tour. Econ., vol. 27, no. 1, pp. 129–148, 2021, doi: 10.1177/1354816619879584.