Assessment of tourist arrival from Russian to Antalya using the univariate time series methods

Year 2021, Volume: 10 Issue: 3, 841 - 848, 17.09.2021

Hatice Öncel Çekim , Ahmet Koyuncu

https://izlik.org/JA76PK97TF

Abstract

Antalya turizminin sürekli büyümesiyle birlikte, daha doğru turizm talep öngörülerine duyulan ihtiyaç ortaya çıkmakta ve öngörü performansı zaman serisi yöntemlerine göre değerlendirilmektir. Mevsimsel dalgalanmalar turizm serilerinin en önemli özelliğidir ve bu özelliği onu farklı modellerin öngörü performanslarını karşılaştırmak için uygun bir ortam haline getirmektedir. Bu çalışmada, 2007-2018 yılları arasında Rusya'dan Antalya'ya gelen turistlerin verileri kullanılmaktadır. Turizm talebinin öngörüsünde parametrik ve parametrik olmayan tek değişkenli zaman serisi teknikleri, ARIMA, ETS, Kombinasyon (veya Hibrit) ve SSA, karşılaştırılmaktadır. Bu çalışma sonucunda elde edilen tahminlerin doğruluğu açısından parametrik olmayan SSA yönteminin daha başarılı olduğu görülmektedir.

Keywords

Turizm , öngörü , zaman serisi yöntemleri , tek değişkenli modeller

References

• [1] World Travel & Tourism Council (WTTC), 2017. City travel & tourism. https://www.wttc.org/-/media/files/reports/special-and-periodic-reports. (access date: 25.09.2020).
• [2] World Travel & Tourism Council (WTTC), 2018. Travel & Tourism. https://dossierturismo.files.wordpress.com/2018/03/wttc-global-economic-impact-and-issues-2018-eng.pdf. (access date: 15.10.2020).
• [3] Kim, J. H., Wong, K., Athanasopoulos, G., Liu, S. 2011. Beyond point forecasting: evaluation of alternative prediction intervals for tourist arrivals. International Journal of Forecasting, 27 (3): 887-901.
• [4] Alvarez-Diaz, M., Rossello-Nadal, J. 2010. Forecasting British tourist arrivals in the Balearic Islands using meteorological variables. Tourism Economics, 16 (1): 153-168.
• [5] De Livera, A. M., Hyndman, R. J., Snyder, R. D. 2011. Forecasting time series with complex seasonal patterns using exponential smoothing. Journal of the American Statistical Association, 106 (496): 1513-1527.
• [6] Dong, Z., Yang, D., Reindl, T., Walsh, W. M. 2013. Short-term solar irradiance forecasting using exponential smoothing state space model. Energy, 55: 1104-1113.
• [7] Hyndman, R. J., Koehler, A. B., Snyder, R. D., Grose, S. 2002. A state space framework for automatic forecasting using exponential smoothing methods. International Journal of Forecasting, 18(3), 439-454.
• [8] Lim, C., McAleer, M. 2001. Forecasting tourist arrivals. Annals of Tourism Research, 28 (4): 965-977.
• [9] Bergmeir, C., Hyndman, R. J., Benitez, J. M. 2016. Bagging exponential smoothing methods using STL decomposition and Box-Cox transformation. International journal of forecasting, 32 (2): 303-312.
• [10] Hassani, H., Heravi, S., Zhigljavsky, A. 2009. Forecasting European industrial production with singular spectrum analysis. International journal of forecasting, 25 (1): 103-118.
• [11] Hassani, H., Ghodsi, Z. 2015. A glance at the applications of singular spectrum analysis in gene expression data. Biomolecular detection and quantification, 4: 17-21.
• [12] Khan, M. A., Poskitt, D. S. 2017. Forecasting stochastic processes using singular spectrum analysis: Aspects of the theory and application. International Journal of Forecasting, 33 (1): 199-213.
• [13] Rekapalli, R., Tiwari, R. K. 2014. Windowed SSA (Singular Spectral Analysis) for Geophysical Time Series Analysis. Journal of Geological Resource and Engineering, 3: 167-173.
• [14] Beneki, C., Eeckels, B., Leon, C. 2012. Signal extraction and forecasting of the UK tourism income time series: A singular spectrum analysis approach. Journal of Forecasting, 31 (5): 391-400.
• [15] Bates, J. M., Granger, C. W. 1969. The combination of forecasts. Journal of the Operational Research Society, 20 (4): 451-468.
• [16] Shen, S., Li, G., Song, H. 2011. Combination forecasts of international tourism demand. Annals of Tourism Research, 38 (1): 72-89.
• [17] Lemke, C., Gabrys, B. 2010. Meta-learning for time series forecasting and forecast combination. Neurocomputing, 73 (10-12): 2006-2016.
• [18] Wong, K. K., Song, H., Witt, S. F., Wu, D. C. 2007. Tourism forecasting: To combine or not to combine? Tourism management, 28 (4): 1068-1078.
• [19] TURKSTAT, 2019. Tourism Statistics, TURKSTAT, https://data.tuik.gov.tr/Kategori/GetKategori?p=Egitim,-Kultur,-Spor-ve-Turizm-105. (access date: 05.09.2020).
• [20] Hyndman, R. J., Khandakar, Y. 2008. Automatic time series forecasting: The forecast Package for R. Journal of Statistical Software, 27 (3): 1-22.
• [21] Preez, J., Witt, S. F. 2003. Univariate versus multivariate time series forecasting: an application to international tourism demand. International Journal of Forecasting, 19: 435-451.
• [22] Prideaux, B. 2000. The role of the transport system in destination development. Tourism management, 21 (1): 53-63.
• [23] Beneki, C., Silva, E. S. 2013. Analysing and forecasting european union energy data. International Journal of Energy and Statistics, 1 (2): 127-141.
• [24] Oliveira, E. M., Oliveira, F. L. 2018. Forecasting mid-long term electric energy consumption through bagging ARIMA and exponential smoothing methods. Energy, 144: 776-788.
• [25] Thamanukornsri, C., Tiensuwan, M. 2018. Applications of Box-Jenkins (Seasonal ARIMA) and GARCH models to dengue incidence in Thailand. Model Assisted Statistics and Applications, 13: 95-105.
• [26] Makridakis, S., Wheelwright, S. C., Hyndman, R. J. 2008. Forecasting methods and applications. John Wiley & Sons, New York, 1-656.
• [27] Hyndman, R. J., & Athanasopoulos, A. G. 2018. Forecasting: principles and practice. OTexts, Monash University, Australia, 1-384.
• [28] Hassani, H., Silva, E. S., Antonakakis, N., Filis, G., Gupta, R. 2017. Forecasting accuracy evaluation of tourist arrivals. Annals of Tourism Research, 63: 112-127.
• [29] 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): 33-44.
• [30] Hassani, H. 2007. Singular spectrum analysis: methodology and comparison. Journal of Data Science, 5: 239-257.
• [31] Newbold, P., Granger, C. W. 1974. Experience with forecasting univariate time series and the combination of forecasts. Journal of the Royal Statistical Society, 131-165.
• [32] Jose, V. R., Winkler, R. L. 2008. Simple robust averages of forecasts: Some empirical results. International Journal of Forecasting, 24 (1): 163-169.
• [33] Bunn, D. W. 1975. A Bayesian approach to the linear combination of forecasts. Journal of the Operational Research Society, 26 (2): 325-329.
• [34] Agnew, C. E. 1985. Bayesian consensus forecasts of macroeconomic variables. Journal of Forecasting, 4 (4): 363-376.
• [35] Claveria, O., Torra, S. 2014. Forecasting tourism demand to Catalonia: Neural networks vs. time series models. Economic Modelling, 36: 220-228.