Journals / Doğuş Üniversitesi Dergisi / 2020 / Cilt: 21 - Sayı: 2

Performance Comparison of Holt-Winters and SARIMA Models for Tourism Forecasting in Turkey

Türkiye’de Turizm Tahmini İçin Holt-Winters ve SARIMA Modellerinin Performanslarının Karşılaştırılması

Pages
63–78
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

However, multiplicative model of HW achieved the highest forecasting accuracy followed by SARIMA and additive HW respectively.

📌 3 Key Takeaways

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Objective & Hypothesis:Forecasting the number of tourists coming to Turkey can play a vital role in strategic planning for both private and public sectors.
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Key Findings & Data:In this study, monthly data of foreigners visiting Turkey were collected between the years 2007 and 2018.
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Conclusion & Impact:However, multiplicative model of HW achieved the highest forecasting accuracy followed by SARIMA and additive HW respectively.
🔬Design:Modeling & Simulation
📍Scope:2007 Verileri
⚙️Variables:Holt-Winters · SARIMA · Üstel düzeltme

Abstract

Forecasting the number of tourists coming to Turkey can play a vital role in strategic planning for both private and public sectors. In this study, monthly data of foreigners visiting Turkey were collected between the years 2007 and 2018. The data showed a seasonal behavior with an increasing trend; consequently, two methods were chosen for the study: Holt-Winters HW and Seasonal Autoregressive Integrated Moving Average SARIMA . The objective of the study is to determine the most appropriate forecasting model to achieve a good level of forecasting accuracy. The findings showed that all models provided accurate forecast values according to error measures. However, multiplicative model of HW achieved the highest forecasting accuracy followed by SARIMA and additive HW respectively.

Özet

Türkiye’ye gelen turist sayısını tahmin etmek hem özel sektör hem de kamu sektörü için stratejik planlamada çok önemli bir rol oynayabilir. Bu çalışmada, Türkiye’yi ziyaret eden yabancıların sayısı 2007 ve 2018 yılları arasında aylık olarak alınmıştır. Veri artan bir eğilim ile mevsimsel davranış göstermektedir, bu nedenle çalışma için iki metot seçilmiştir: Holt-Winters HW and Seasonal Autoregressive Integrated Moving Average SARIMA . Çalışmanın amacı iyi bir seviyede tahmin doğruluğu elde etmek için en uygun tahmin modelini belirlemektir. Sonuçlar bütün modellerin hata ölçümlerine göre doğru tahmin değerleri verdiğini göstermiştir. Bununla birlikte, HW çarpımsal modeli en yüksek tahmin doğruluğuna erişmiş, bunu sırasıyla SARIMA ve HW toplamsal modeli takip etmiştir

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Cite This Article

ZAYAT, W., SENNAROĞLU, B. (2020). Performance Comparison of Holt-Winters and SARIMA Models for Tourism Forecasting in Turkey. Doğuş Üniversitesi Dergisi, Cilt: 21 - Sayı: 2, 63–78. https://ajindex.com/articles/125200

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References (10)

  1. Akal, M. (2004). Forecasting Turkey's tourism revenues by ARMAX model. Tourism Management, 25(5), 565-580.
  2. Athanasopoulosa, G., Hyndman, R. J., & Song, H., WU, D. (2011). The tourism forecasting competition. International Journal of Forecasting 27. 822–844. Bermúdeza, J. D., Segurab, J. V., Verchera, E. (2006). A decision support system methodology for forecasting of time series based on soft computing. Computational Statistics & Data Analysis. Volume 51, Issue 1, 177-191.
  3. Box, G., & Jenkins, G. (1970). Time Series Analysis-Forecasting and Control. San Francisco: Holden Day. 553 p. Brown, R. G., Meyer, R. F. (1961). The fundamental theory of exponential smoothing, Operations Research, 9, p. 673-685.
  4. Change, Y. W., Liao, M. Y., (2010). A Seasonal ARIMA Model of Tourism Forecasting: The Case of Taiwan. Asia Pacific Journal of Tourism Research, 15:2, 215-221.
  5. Close L. Jian, Y. Zhao, Y.P. Zhu, M.B. Zhang, D.Bertolatti. (2012). An application of ARIMA model to predict submicron particle concentrations from meteorological factors at a busy roadside in Hangzhou, China. Sci. Total Environ., 426, pp. 336-345.
  6. Cuhadar, M., Cogurcu, I., & Kukrer, C. (2014). Modelling and forecasting cruise tourism demand to Izmir by different artificial neural network architectures. International Journal of Business and Social Research, 4(3), 12-28.
  7. Dhahri, I., & Chabchoub, H., (2007). Nonlinear goal programming models quantifying the bullwhip effect in supply chain based on ARIMA parameters. European Journal of Operational Research, 177 (3), 1800–1810.
  8. Holt, C. C. (1957). Forecasting seasonals and trends by exponentially weighted averages O.N.R. Memorandum No. 52. Carnegie Institute of Technology, Pittsburgh USA. Holt, C. C. (2004) Forecasting seasonals and trends by exponentially weighted moving averages. International Journal of Forecasting, 20, 5–10.
  9. Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: principles and practice. OTexts. Wheelwright, S., Makridakis, S., & Hyndman, R. J. (1998). Forecasting: methods and applications. John Wiley & Sons. Hyndman, R.J. & Koehler, A.B. & Snyder, R.D. & Grose, S (2002). A state space framework automatic forecasting using exponential smoothing. Int. J. Forecasting, 18, pp. 439-454.
  10. Montgomery, D. C., Johnson, L. A., & Gardiner, J. S. (1990). Forecasting and time series analysis (p. 151). New York etc.: McGraw-Hill. Moss, S., Liu, J., & Moss, J. (2013). Issues in forecasting international tourist travel. Journal of Management Informa