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The Impact of Google Trends on the Tourist Arrivals: A Case of Antalya Tourism

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  • Hatice Öncel Çekim
  • Ahmet Koyuncu

Abstract

With the growth of the tourism industry, tourism demand forecasting has become an important research topic. Recently researches have shown that Google Trends(GT) data with the help of Google can positively affect the forecast of tourist arrivals. However, the use of this data directly can cause some errors. This article provides suggestions on how the calculation differences according to the same time at different time intervals in GT data (which is obtained on an hourly, daily, monthly and yearly basis) can be eliminated. In this study, it is aimed to examine the effect of GT data for Antalya, Turkey's favorite tourist destination by the Russians. In addition, the multivariate time series models are used to see separately and together the effects of international trade (IT), weather conditions (WC) and number of flights (FN) variables on tourism data, as well as GT data. As a result, it has been seen that the tourist arrival can be forecasted better with the GT (AGT) data, which is recommended to be used by adjusted.

Suggested Citation

  • Hatice Öncel Çekim & Ahmet Koyuncu, 2022. "The Impact of Google Trends on the Tourist Arrivals: A Case of Antalya Tourism," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 10(1), pages 1-14, June.
    • Handle: RePEc:anm:alpnmr:v:10:y:2022:i:1:p:1-14
    DOI: https://doi.org/10.17093/alphanumeric.931652
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    References listed on IDEAS

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    1. Hyunyoung Choi & Hal Varian, 2012. "Predicting the Present with Google Trends," The Economic Record, The Economic Society of Australia, vol. 88(s1), pages 2-9, June.
    2. Song, Haiyan & Li, Gang & Witt, Stephen F. & Athanasopoulos, George, 2011. "Forecasting tourist arrivals using time-varying parameter structural time series models," International Journal of Forecasting, Elsevier, vol. 27(3), pages 855-869.
    3. Mansi Ghodsi & Hossein Hassani & Donya Rahmani & Emmanuel Sirimal Silva, 2018. "Vector and recurrent singular spectrum analysis: which is better at forecasting?," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(10), pages 1872-1899, July.
    4. Hossein Hassani & Emmanuel Sirimal Silva, 2015. "A Kolmogorov-Smirnov Based Test for Comparing the Predictive Accuracy of Two Sets of Forecasts," Econometrics, MDPI, vol. 3(3), pages 1-20, August.
    5. Haiyan Song & Gang Li & Stephen F. Witt & Baogang Fei, 2010. "Tourism Demand Modelling and Forecasting: How Should Demand Be Measured?," Tourism Economics, , vol. 16(1), pages 63-81, March.
    6. Tomas Havranek & Ayaz Zeynalov, 2021. "Forecasting tourist arrivals: Google Trends meets mixed-frequency data," Tourism Economics, , vol. 27(1), pages 129-148, February.
    7. Bangwayo-Skeete, Prosper F. & Skeete, Ryan W., 2015. "Can Google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approach," Tourism Management, Elsevier, vol. 46(C), pages 454-464.
    8. Andrawis, Robert R. & Atiya, Amir F. & El-Shishiny, Hisham, 2011. "Combination of long term and short term forecasts, with application to tourism demand forecasting," International Journal of Forecasting, Elsevier, vol. 27(3), pages 870-886, July.
    9. Silva, Emmanuel Sirimal & Ghodsi, Zara & Ghodsi, Mansi & Heravi, Saeed & Hassani, Hossein, 2017. "Cross country relations in European tourist arrivals," Annals of Tourism Research, Elsevier, vol. 63(C), pages 151-168.
    10. 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.
    11. Hossein Hassani & Saeed Heravi & Anatoly Zhigljavsky, 2013. "Forecasting UK Industrial Production with Multivariate Singular Spectrum Analysis," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(5), pages 395-408, August.
    12. Kerry Patterson & Hossein Hassani & Saeed Heravi & Anatoly Zhigljavsky, 2011. "Multivariate singular spectrum analysis for forecasting revisions to real-time data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(10), pages 2183-2211.
    13. Hassani, Hossein & Silva, Emmanuel Sirimal & Antonakakis, Nikolaos & Filis, George & Gupta, Rangan, 2017. "Forecasting accuracy evaluation of tourist arrivals," Annals of Tourism Research, Elsevier, vol. 63(C), pages 112-127.
    14. Balli, Faruk & Balli, Hatice Ozer & Cebeci, Kemal, 2013. "Impacts of exported Turkish soap operas and visa-free entry on inbound tourism to Turkey," Tourism Management, Elsevier, vol. 37(C), pages 186-192.
    15. Bieger, Thomas & Wittmer, Andreas, 2006. "Air transport and tourism—Perspectives and challenges for destinations, airlines and governments," Journal of Air Transport Management, Elsevier, vol. 12(1), pages 40-46.
    16. Rivera, Roberto, 2016. "A dynamic linear model to forecast hotel registrations in Puerto Rico using Google Trends data," Tourism Management, Elsevier, vol. 57(C), pages 12-20.
    17. Andrawis, Robert R. & Atiya, Amir F. & El-Shishiny, Hisham, 2011. "Combination of long term and short term forecasts, with application to tourism demand forecasting," International Journal of Forecasting, Elsevier, vol. 27(3), pages 870-886.
    18. Emmanuel Sirimal Silva & Hossein Hassani & Saeed Heravi, 2018. "Modeling European industrial production with multivariate singular spectrum analysis: A cross†industry analysis," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(3), pages 371-384, April.
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    More about this item

    Keywords

    Google Trends; ARIMAX; MSSA; Multivariate Models; Tourism Forecast;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics

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