Assessment of tourist arrival from Russian to Antalya using the univariate time series methods
Yıl 2021,
Cilt: 10 Sayı: 3, 841 - 848, 17.09.2021
Hatice Öncel Çekim
,
Ahmet Koyuncu
Öz
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.
Kaynakça
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Yıl 2021,
Cilt: 10 Sayı: 3, 841 - 848, 17.09.2021
Hatice Öncel Çekim
,
Ahmet Koyuncu
Kaynakça
- [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.
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- [23] Beneki, C., Silva, E. S. 2013. Analysing and forecasting european union energy data. International Journal of Energy and Statistics, 1 (2): 127-141.
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- [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.
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- [35] Claveria, O., Torra, S. 2014. Forecasting tourism demand to Catalonia: Neural networks vs. time series models. Economic Modelling, 36: 220-228.