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

Öncel Çekim, Hatice; Koyuncu, Ahmet

doi:https://doi.org/10.17093/alphanumeric.931652

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

Hatice Öncel Çekim, Ph.D.

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.

Keywords: Google Trends, ARIMAX, MSSA, Multivariate Models, Tourism Forecast

Jel Classification: C01

Suggested citation

Öncel Çekim, H. & Koyuncu, A. (2022). The Impact of Google Trends on the Tourist Arrivals: A Case of Antalya Tourism. Alphanumeric Journal, 10(1), 1-14. https://doi.org/10.17093/alphanumeric.931652

bibtex

References

Aksu, A., Ucar, O., & Kilicarslan, D. (2017). Golf Tourism: A Research Profile and Security Perceptions in Belek, Antalya, Turkey. International Journal of Business and Social Research, 6(12), pp. 1-12.
Álvarez-Díaz, M., & Rosselló-Nadal, J. (2010). Forecasting British tourist arrivals in the Balearic Islands using meteorological variables. Tourism Economics, 16(1), 153-168.
Andrawis, R. R., Atiya, A. F., & El-Shishiny, H. (2011). Combination of long term and short term forecasts, with application to tourism demand forecasting. International Journal of Forecasting, 27(3), pp. 870-886.
Anggraeni, W., Vinarti, R. A., & Kurniawati, Y. D. (2015). Performance comparisons between arima and arimax method in moslem kids clothes demand forecasting: Case study. Procedia Computer Science, 72, pp. 630-637.
Balli, F., Balli, H. O., & Cebeci, K. (2013). Impacts of exported Turkish soap operas and visa-free entry on inbound tourism to Turkey. Tourism Management, 37, pp. 186-192.
Bangwayo-Skeete, P. F., & Skeete, R. W. (2015). Can Google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approach, 46, pp. 454-464.
Bieger, T., & Wittmer, A. (2006). Air transport and tourism perspectives and challenges for destinations, airlines and governments. Journal of air transport management, 12(1), pp. 40-46.
Cakar, K. (2018). Critical success factors for tourist destination governance in times of crisis: a case study of Antalya, Turkey. Journal of Travel & Tourism Marketing, 35(6), pp. 786-802.
Cekim, H. O., & Koyuncu, A. Assessment of tourist arrival from Russian to Antalya using the univariate time series methods. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, 10(3), pp. 841-848.
Chatfield, C. (2000). Time-series forecasting. CRC Press.
Choi, H., & Varian, H. (2012). Predicting the present with Google Trends. Economic Record, 88, pp. 2-9.
Chu, F. L. (2008). A fractionally integrated autoregressive moving average approach to forecasting tourism demand. Tourism Management, 29(1), pp. 79-88.
Demir, E., & Gozgor, G. (2017). What about relative corruption? The impact of the relative corruption on the inbound tourism to Turkey. International Journal of Tourism Research, 19(3), pp. 358-366.
Dergiades, T., Mavragani, E., & Pan, B. (2018). Google Trends and tourists' arrivals: Emerging biases and proposed corrections. Tourism Management, 66, 108-120.
DHMI. (2019). https://www.dhmi.gov.tr/sayfalar/istatistik.aspx/. DHMI.
Dinis, G., Breda, Z., Costa, C., & Pacheco, O. (2019). Google Trends in tourism and hospitality research: a systematic literature review. Journal of Hospitality and Tourism Technology.
ECM & MU (2015). The European Cities Marketing Benchmarking Report, 11th Official Edition 2014-2015. Dijon, France: European Cities Marketing (ECM).
Ghil, M., Allen, M. R., Dettinger, M. D., Ide, K., Kondrashov, D., Mann, M. E., & Yiou, P. (2002). Advanced spectral methods for climatic time series. Reviews of geophysics, 40(1), pp. 3-1.
Ghodsi, M., Hassani, H., Rahmani, D., & Silva, E. S. (2018). Vector and recurrent singular spectrum analysis: which is better at forecasting? Journal of Applied Statistics, 45(10), pp. 1872-1899.
Gil-Alana, L. A. (2005). Modelling international monthly arrivals using seasonal univariate long-memory processes. Tourism Management, 26(6), pp. 867-878.
Gounopoulos, D., Petmezas, D., & Santamaria, D. (2012). Forecasting tourist arrivals in Greece and the impact of macroeconomic shocks from the countries of tourists’ origin. Annals of Tourism Research, 39(2), pp. 641-666.
Greenidge, K. (2001). Forecasting tourism demand: An STM approach. Annals of Tourism Research, 28(1), pp. 98-112.
Groth, A., & Ghil, M. (2011). Multivariate singular spectrum analysis and the road to phase synchronization. Physical Review E, 84(3), pp. 1-12.
Hadavandi, E., Ghanbari, A., Shahanaghi, K., & Abbasian-Naghneh, S. (2011). Tourist arrival forecasting by evolutionary fuzzy systems. Tourism Management, 32(5), pp. 1196-1203.
Hassani, H., & Mahmoudvand, R. (2013). Multivariate singular spectrum analysis: A general view and new vector forecasting approach. International Journal of Energy and Statistics, 1, p. 01.
Hassani, H., & Silva, E. S. (2015). A Kolmogorov-Smirnov based test for comparing the predictive accuracy of two sets of forecasts. Econometrics, 3(3), 590-609.
Hassani, H., Heravi, S., & Zhigljavsky, A. (2013). Forecasting UK industrial production with multivariate singular spectrum analysis. Journal of Forecasting, 32(5), pp. 395-408.
Hassani, H., Silva, E. S., Antonakakis, N., Filis, G., & Gupta, R. (2017). Forecasting accuracy evaluation of tourist arrivals. Annals of Tourism Research, 63, pp. 112-127.
Hassani, H., Soofi, A. S., & Zhigljavsky, A. A. (2010). Predicting daily exchange rate with singular spectrum analysis. Nonlinear Analysis: Real World Applications, 11(3), pp. 2023-2034.
Havranek, T., & Zeynalov, A. (2021). Forecasting tourist arrivals: Google Trends meets mixed-frequency data. Tourism Economics, 27(1), 129-148.
Ihueze, C. C., & Onwurah, U. O. (2018). Road traffic accidents prediction modelling: An analysis of Anambra State, Nigeria. Accident Analysis & Prevention, 112, pp. 21-29.
Jackman, M., & Greenidge, K. (2010). Modelling and forecasting tourist flows to Barbados using structural time series models. Tourism and Hospitality Research, 10(1), pp. 1-13.
Jalalkamali, A., Moradi, M., & Moradi, N. (2015). Application of several artificial intelligence models and ARIMAX model for forecasting drought using the Standardized Precipitation Index. International journal of environmental science and technology, 12(4), pp. 1201-1210.
Jifri, M. H., Hassan, E. E., & Miswan, N. H. (2018). Investigate the forecasting technique for electricity load demand in urban area via statistical approach. Journal of Fundamental and Applied Sciences, 10(5), pp. 298-310.
Kim, W. H., & Malek, K. (2018). Forecasting casino revenue by incorporating Google trends. International Journal of Tourism Research, 20(4), pp. 424-432.
Kozak, N., Uysal, M., & Birkan, I. (2008). An analysis of cities based on tourism supply and climatic conditions in Turkey. Tourism Geographies, 10(1), pp. 81-97.
Lim, C., & McAleer, M. (2001). Forecasting tourist arrivals. Annals of Tourism Research, 28(4), pp. 965-977.
MGM. (2019). https://www.mgm.gov.tr/veridegerlendirme/sicaklik-analizi.aspx. MGM.
Mokhtarzad, M., Eskandari, F., Vanjani, N. J., & Arabasadi, A. (2017). Drought forecasting by ANN, ANFIS, and SVM and comparison of the models. Environmental Earth Sciences, 76(21), p. 729.
Molina, L. L., Angon, E., Garcia, A., Moralejo, R. H., Caballero-Villalobos, J., & Perea, J. (2018). Time series analysis of bovine venereal diseases in La Pampa, Argentina. PloS one, 13(8), pp. 1-17.
Morabito, M., Cecchi, L., Modesti, P. A., Crisci, A., Orlandini, S., Maracchi, G., & Gensini, G. F. (2004). The impact of hot weather conditions on tourism in Florence, Italy: the summer 2002-2003 experience. Advances in tourism climatology, 12, pp. 158-165.
Naim, I., & Mahara, T. (2018). Comparative Analysis of Univariate Forecasting Techniques for Industrial Natural Gas Consumption. International Journal of Image, Graphics & Signal Processing, 10(5), pp. 33-44.
Olya, H. G., Shahmirzdi, E. K., & Alipour, H. (2017). Pro-tourism and anti-tourism community groups at a world heritage site in Turkey. Current Issues in Tourism, pp. 1-23.
Oropeza, V., & Sacchi, M. (2011). Simultaneous seismic data denoising and reconstruction via multichannel singular spectrum analysis. Geophysics, 76(3), pp. V25-V32.
Ozdemir, B., Cizel, B., & Bato Cizel, R. (2012). Satisfaction with all-inclusive tourism resorts: The effects of satisfaction with destination and destination loyalty. International Journal of Hospitality & Tourism Administration, 13(2), pp. 109-130.
Pan, B., & Yang, Y. (2017). Forecasting destination weekly hotel occupancy with big data. Journal of Travel Research, 56(7), pp. 957-970.
Patterson, K., Hassani, H., Heravi, S., & Zhigljavsky, A. (2011). Multivariate singular spectrum analysis for forecasting revisions to real-time data. Journal of Applied Statistics, 38(10), pp. 2183-2211.
Poghosyan, H. (2018). The effectiveness of economic sanctions: the case of Russia sanctions against Turkey. Doctoral dissertation, Tartu Aoelikoo.
Rivera, R. (2016). A dynamic linear model to forecast hotel registrations in Puerto Rico using Google Trends data. Tourism Management, 57, pp. 12-20.
Shareef, R., & McAleer, M. (2007). Modelling the uncertainty in monthly international tourist arrivals to the Maldives. Tourism Management, 28(1), pp. 23-45.
Siami-Namini, S., Tavakoli, N., & Namin, A. S. (2018). A comparison of ARIMA and LSTM in forecasting time series. In 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, pp. 1394-1401.
Silva, E. S., Ghodsi, Z., Ghodsi, M., Heravi, S., & Hassani, H. (2017). Cross country relations in European tourist arrivals. Annals of Tourism Research, 63, pp. 151-168.
Silva, E. S., Hassani, H., & Heravi, S. (2018). Modeling European industrial production with multivariate singular spectrum analysis: A cross-industry analysis. Journal of Forecasting, 37(3), pp. 371-384.
Silva, E. S., Hassani, H., Heravi, S., & Huang, X. (2019). Forecasting tourism demand with denoised neural networks. Annals of Tourism, 74, 134-154.
Song, H., Li, G., Witt, S. F., & Athanasopoulos, G. (2011). Forecasting tourist arrivals using time-varying parameter structural time series models. International Journal of Forecasting, 27(3), pp. 855-869.
Song, H., Li, G., Witt, S. F., & Fei, B. (2010). Tourism demand modelling and forecasting: how should demand be measured? Tourism Economics, 16(1), pp. 63-81.
TURKSTAT. (2019). Toursim Statistics. TURKSTAT.
World Bank. (2019). https://databank.worldbank.org/data/reports.aspx?source=1147&series=IT.NET.USER. World Bank.
World Travel & Tourism Council (WTTC). (2017). City travel & tourism. World Travel & Tourism Council (WTTC). https://www.wttc.org/-/media/files/reports/special-and-periodic-reports.
World Travel & Tourism Council (WTTC). (2018). Travel & Tourism. World Travel & Tourism Council (WTTC). https://dossierturismo.files.wordpress.com/2018/03/wttc-global-economic-impact-and-issues-2018-eng.pdf
Yang, Y., Pan, B., & Song, H. (2014). Predicting hotel demand using destination marketing organization’s web traffic data. Journal of Travel Research, 53(4), pp. 433-447.
Yi, Z., & Xiao-feng, H. (2012). Research on daily exchange rate forecasting with multivariate singular spectrum analysis. In Management Science and Engineering (ICMSE), International Conference on (pp. 1365-1370). IEEE.