MODELING AND FORECASTING OF TOURISM INCOME: THE CASE OF TURKEY

Begüm Kara Gülay

doi:10.18070/erciyesiibd.1581119

Research Article

TURİZM GELİRLERİNİN MODELLENMESİ VE TAHMİN EDİLMESİ: TÜRKİYE ÖRNEĞİ

Year 2024, Issue: 69, 251 - 257, 30.12.2024

Begüm Kara Gülay

https://doi.org/10.18070/erciyesiibd.1581119

Abstract

Bu çalışmanın amacı, ekonomik planlama ve kalkınma için önemli bir faktör olan Türkiye'nin turizm gelirini tahmin etmek için optimum tahmin modelini belirlemektir. Mevsimsel Otoregresif Entegre Hareketli Ortalama (SARIMA), Holt-Winters yöntemleri (eklemeli ve çarpımsal), Durum Uzay Modelleri (ETS), Yapay Sinir Ağları (YSA) ve mevsimsel eğilim ayrıştırma (STL) -ANN hibrit modeli dahil olmak üzere tahmin tekniklerini kullanmak ve bunların performansını değerlendirmektedir. Metodoloji, Ocak 2012'den Aralık 2023'e kadar aylık turizm geliri verilerinin analiz edilmesini ve YSA modeli için ziyaretçi sayıları, ekonomik güven endeksi, tüketici fiyat endeksi, endüstriyel üretim endeksi ve ABD doları döviz kuru gibi ek ekonomik göstergelerin dahil edilmesini içermektedir. Bulgular, özellikle turizm gelirini diğer ekonomik göstergelerle birlikte içeren model olan YSA'ların, en düşük Ortalama Mutlak Ölçekli Hata ve Kök Ortalama Karesel Ölçekli Hata ile geleneksel modellerden daha iyi performans gösterdiğini ortaya koymaktadır. Özellikle, ek öngörücülere sahip YSA modeli en yüksek tahmin doğruluğunu göstermiştir. Bu sonuçlar, gelişmiş makine öğrenme tekniklerinin geleneksel doğrusal modellere kıyasla üstün tahmin yetenekleri sağladığını göstermektedir. Çalışma, daha doğru tahminler için karmaşık modellerin entegre edilmesinin önemini vurgulayarak, turizm sektöründeki politika yapıcılar ve uygulayıcılar için değerli sonuçlar sunmaktadır.

Keywords

Turizm Geliri, Öngörümleme, Yapay Sinir Ağları, Zaman Serisi Analizi

References

Akal, M. (2004). Forecasting Türkiye's tourism revenues by ARMAX model. Tourism Management, 25(5), 565-580.
Apaydin, H., Sattari, M. T., Falsafian, K., & Prasad, R. (2021). Artificial intelligence modelling integrated with Singular Spectral analysis and Seasonal-Trend decomposition using Loess approaches for streamflow predictions. Journal of Hydrology, 600, 126506.
Aydin, O. (2016). Tourism Income of Türkiye: A panel data approach. Procedia economics and finance, 38, 245-256.
Burger, C. J. S. C., Dohnal, M., Kathrada, M., & Law, R. (2001). A practitioners guide to time-series methods for tourism demand forecasting—a case study of Durban, South Africa. Tourism management, 22(4), 403-409.
Chatfield, C., Koehler, A. B., Ord, J. K., & Snyder, R. D. (2001). A new look at models for exponential smoothing. Journal of the Royal Statistical Society: Series D (The Statistician), 50(2), 147-159.
Chen, K. Y., & Wang, C. H. (2007). Support vector regression with genetic algorithms in forecasting tourism demand. Tourism management, 28(1), 215-226.
Cho, V. (2003). A comparison of three different approaches to tourist arrival forecasting. Tourism management, 24(3), 323-330.
Çuhadar, M., Güngör, P., & Göksu, Y. (2009). Turizm talebinin yapay sinir ağları ile tahmini ve zaman serisi yöntemleri ile karşılaştırmalı analizi: Antalya iline yönelik bir uygulama [Forecasting tourism demand using artificial neural networks and comparative analysis with time series methods: An application for Antalya]. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 14(1), 99–114.
Çuhadar, M. (2020). A comparative study on modelling and forecasting tourism revenues: The case of Türkiye. Advances in Hospitality and Tourism Research (AHTR), 8(2), 235-255.
Dritsakis, N. (2012). Tourism development and economic growth in seven Mediterranean countries: A panel data approach. Tourism Economics, 18(4), 801-816.
Goh, C., & Law, R. (2002). Modeling and forecasting tourism demand for arrivals with stochastic nonstationary seasonality and intervention. Tourism Management, 33(4), 819-829.
Hyndman, R., Koehler, A. B., Ord, J. K., & Snyder, R. D. (2008). Forecasting with exponential smoothing: the state space approach. Springer Science & Business Media.
Hossen, S. M., Ismail, M. T., Tabash, M. I., & Anagreh, S. (2022). Do tourist arrivals in Bangladesh depend on seasonality in humidity? A SARIMA and SANCOVA approach. Geo Journal of Tourism and Geosites, 41(2), 606-613.
Karahan, M. (2015). Turizm talebinin yapay sinir ağları yöntemiyle tahmin edilmesi [Forecasting tourism demand using the artificial neural networks method]. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 20(2), 195–209.
Kayakuş, M., Erdoğan, D., & Terzioğlu, M. (2023). Predicting the share of tourism revenues in total exports. Alphanumeric Journal, 11(1), 17-30.
Kayral, İ. E., Sarı, T., & Tandoğan Aktepe, N. Ş. (2023). Forecasting the Tourist Arrival Volumes and Tourism Income with Combined ANN Architecture in the Post COVID-19 Period: The Case of Türkiye. Sustainability, 15(22), 15924.
Kecman, V. (2001). Learning and soft computing: support vector machines, neural networks, and fuzzy logic models. MIT press.
Kontogianni, A., Alepis, E., & Patsakis, C. (2022). Promoting smart tourism personalised services via a combination of deep learning techniques. Expert Systems with Applications, 187, 115964.
Koyuncu, O., Gozlu, M., & Atici, K. B. (2016). Analysis and forecasts on the healthcare tourism income of Türkiye. Journal of Economics Finance and Accounting, 3(3), 222-233.
Kůrková, V. (1992). Kolmogorov's theorem and multilayer neural networks. Neural networks, 5(3), 501-506.
Law, R., & Au, N. (1999). A neural network model to forecast Japanese demand for travel to Hong Kong. Tourism Management, 20(1), 89-97.
Li, X., Law, R., Xie, G., & Wang, S. (2021). Review of tourism forecasting research with internet data. Tourism Management, 83, 104245.
Muhaimin, A., Prastyo, D. D., & Lu, H. H. S. (2021, January). Forecasting with recurrent neural network in intermittent demand data. In 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence) (pp. 802-809). IEEE.
Palmer, A., Montano, J. J., & Sesé, A. (2006). Designing an artificial neural network for forecasting tourism time series. Tourism Management, 27(5), 781-790.
Republic of Türkiye Ministry of Culture and Tourism. (2023). Türkiye tourism strategy. Retrieved from https://www.ktb.gov.tr/TR-96696/turkiye-turizm-stratejisi.html
Song, H., & Li, G. (2008). Tourism demand modelling and forecasting-A review of recent research. Tourism Management, 29(2), 203-220.
Song, H., Witt, S. F., & Jensen, T. C. (2003). Tourism forecasting: accuracy of alternative econometric models. International Journal of Forecasting, 19(1), 123-141.
Sönmez, S., & Sirakaya, E. (2002). A distorted destination image? The case of Türkiye. Journal of travel research, 41(2), 185-196.
Tuncsiper, C. (2023). Modeling the Tourism Revenue of Türkiye Using Deep Learning Networks. Open Access Indonesia Journal of Social Sciences, 6(1), 888-897.
Thomas, A. J., Petridis, M., Walters, S. D., Gheytassi, S. M., & Morgan, R. E. (2017). Two hidden layers are usually better than one. In Engineering Applications of Neural Networks: 18th International Conference, EANN 2017, Athens, Greece, August 25–27, 2017, Proceedings (pp. 279-290). Springer International Publishing.
Uysal, M., & El Roubi, M. S. (1999). Artificial neural networks versus multiple regression in tourism demand analysis. Journal of Travel Research, 38(2), 111-118.
Yenişehirlioğlu, E., Taşar, İ., & Bayat, T. (2020). Tourism Revenue and Economic Growth Relation in Türkiye: Evidence of Symmetrical, Asymmetrical and the Rolling Window Regressions. Journal of Economic Cooperation & Development, 41(2).
Wong, K. K. F., Song, H., & Chon, K. K. S. (2006). Bayesian models for tourism demand forecasting. Tourism Management, 27(5), 773-780.
World Travel & Tourism Council (WTTC). (2023). Türkiye (Turkey) travel & tourism economic impact factsheet. Retrieved from https://cdn.prod.website-files.com/6329bc97af73223b575983ac/66695b1694d436e5830238da_Turkiye2024_.pdf
World Travel & Tourism Council (WTTC). (2024). Travel & tourism set to break all records in 2024. Retrieved from https://wttc.org/news-article/travel-and-tourism-set-to-break-all-records-in-2024-reveals-wttc
Zorlutuna, Ş., & Bircan, H. (2019). Türkiye’ye gelen turist sayısı tahmininde zaman serileri analizi ve yapay sinir ağları yöntemlerinin karşılaştırılması [Comparison of time series analysis and artificial neural network methods in forecasting the number of tourists visiting Türkiye]. Cumhuriyet Üniversitesi İktisadi ve İdari Bilimler Dergisi, 20(2), 164–185.

MODELING AND FORECASTING OF TOURISM INCOME: THE CASE OF TURKEY

Year 2024, Issue: 69, 251 - 257, 30.12.2024

Begüm Kara Gülay

https://doi.org/10.18070/erciyesiibd.1581119

Abstract

This study aims to identify the optimal forecasting model for predicting Türkiye's tourism income, a crucial factor for economic planning and development. This study employs different forecasting techniques, including the seasonal Autoregressive Integrated Moving Average (SARIMA), the additive and multiplicative Holt-Winters methods, the Exponential Smoothing State Space (ETS), Artificial Neural Networks (ANNs) and seasonal-trend decomposition procedure based on the loess (STL)-ANN hybrid model and evaluates their performance. The methodology involves analyzing monthly tourism income data from January 2012 to December 2023, incorporating additional economic indicators such as the economic confidence index, number of visitors, consumer price index, industrial production index, and USD exchange rate, which serve as input for ANN models. The findings reveal that ANNs, particularly the model that incorporates tourism income alongside other economic indicators, outperform traditional models with the lowest Mean Absolute Scaled Error (MASE) and Root Mean Squared Scaled Error (RMSSE). Specifically, the ANN model with additional predictors demonstrates the highest forecasting accuracy. These results suggest that advanced machine learning techniques provide superior predictive capabilities compared to traditional linear models. The study underscores the importance of integrating complex models to achieve more accurate forecasting, offering valuable insights for policymakers and practitioners in the tourism sector.

Keywords

Tourism Income, Forecasting, Artificial Neural Networks, Time Series Analysis

References

Akal, M. (2004). Forecasting Türkiye's tourism revenues by ARMAX model. Tourism Management, 25(5), 565-580.
Apaydin, H., Sattari, M. T., Falsafian, K., & Prasad, R. (2021). Artificial intelligence modelling integrated with Singular Spectral analysis and Seasonal-Trend decomposition using Loess approaches for streamflow predictions. Journal of Hydrology, 600, 126506.
Aydin, O. (2016). Tourism Income of Türkiye: A panel data approach. Procedia economics and finance, 38, 245-256.
Burger, C. J. S. C., Dohnal, M., Kathrada, M., & Law, R. (2001). A practitioners guide to time-series methods for tourism demand forecasting—a case study of Durban, South Africa. Tourism management, 22(4), 403-409.
Chatfield, C., Koehler, A. B., Ord, J. K., & Snyder, R. D. (2001). A new look at models for exponential smoothing. Journal of the Royal Statistical Society: Series D (The Statistician), 50(2), 147-159.
Chen, K. Y., & Wang, C. H. (2007). Support vector regression with genetic algorithms in forecasting tourism demand. Tourism management, 28(1), 215-226.
Cho, V. (2003). A comparison of three different approaches to tourist arrival forecasting. Tourism management, 24(3), 323-330.
Çuhadar, M., Güngör, P., & Göksu, Y. (2009). Turizm talebinin yapay sinir ağları ile tahmini ve zaman serisi yöntemleri ile karşılaştırmalı analizi: Antalya iline yönelik bir uygulama [Forecasting tourism demand using artificial neural networks and comparative analysis with time series methods: An application for Antalya]. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 14(1), 99–114.
Çuhadar, M. (2020). A comparative study on modelling and forecasting tourism revenues: The case of Türkiye. Advances in Hospitality and Tourism Research (AHTR), 8(2), 235-255.
Dritsakis, N. (2012). Tourism development and economic growth in seven Mediterranean countries: A panel data approach. Tourism Economics, 18(4), 801-816.
Goh, C., & Law, R. (2002). Modeling and forecasting tourism demand for arrivals with stochastic nonstationary seasonality and intervention. Tourism Management, 33(4), 819-829.
Hyndman, R., Koehler, A. B., Ord, J. K., & Snyder, R. D. (2008). Forecasting with exponential smoothing: the state space approach. Springer Science & Business Media.
Hossen, S. M., Ismail, M. T., Tabash, M. I., & Anagreh, S. (2022). Do tourist arrivals in Bangladesh depend on seasonality in humidity? A SARIMA and SANCOVA approach. Geo Journal of Tourism and Geosites, 41(2), 606-613.
Karahan, M. (2015). Turizm talebinin yapay sinir ağları yöntemiyle tahmin edilmesi [Forecasting tourism demand using the artificial neural networks method]. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 20(2), 195–209.
Kayakuş, M., Erdoğan, D., & Terzioğlu, M. (2023). Predicting the share of tourism revenues in total exports. Alphanumeric Journal, 11(1), 17-30.
Kayral, İ. E., Sarı, T., & Tandoğan Aktepe, N. Ş. (2023). Forecasting the Tourist Arrival Volumes and Tourism Income with Combined ANN Architecture in the Post COVID-19 Period: The Case of Türkiye. Sustainability, 15(22), 15924.
Kecman, V. (2001). Learning and soft computing: support vector machines, neural networks, and fuzzy logic models. MIT press.
Kontogianni, A., Alepis, E., & Patsakis, C. (2022). Promoting smart tourism personalised services via a combination of deep learning techniques. Expert Systems with Applications, 187, 115964.
Koyuncu, O., Gozlu, M., & Atici, K. B. (2016). Analysis and forecasts on the healthcare tourism income of Türkiye. Journal of Economics Finance and Accounting, 3(3), 222-233.
Kůrková, V. (1992). Kolmogorov's theorem and multilayer neural networks. Neural networks, 5(3), 501-506.
Law, R., & Au, N. (1999). A neural network model to forecast Japanese demand for travel to Hong Kong. Tourism Management, 20(1), 89-97.
Li, X., Law, R., Xie, G., & Wang, S. (2021). Review of tourism forecasting research with internet data. Tourism Management, 83, 104245.
Muhaimin, A., Prastyo, D. D., & Lu, H. H. S. (2021, January). Forecasting with recurrent neural network in intermittent demand data. In 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence) (pp. 802-809). IEEE.
Palmer, A., Montano, J. J., & Sesé, A. (2006). Designing an artificial neural network for forecasting tourism time series. Tourism Management, 27(5), 781-790.
Republic of Türkiye Ministry of Culture and Tourism. (2023). Türkiye tourism strategy. Retrieved from https://www.ktb.gov.tr/TR-96696/turkiye-turizm-stratejisi.html
Song, H., & Li, G. (2008). Tourism demand modelling and forecasting-A review of recent research. Tourism Management, 29(2), 203-220.
Song, H., Witt, S. F., & Jensen, T. C. (2003). Tourism forecasting: accuracy of alternative econometric models. International Journal of Forecasting, 19(1), 123-141.
Sönmez, S., & Sirakaya, E. (2002). A distorted destination image? The case of Türkiye. Journal of travel research, 41(2), 185-196.
Tuncsiper, C. (2023). Modeling the Tourism Revenue of Türkiye Using Deep Learning Networks. Open Access Indonesia Journal of Social Sciences, 6(1), 888-897.
Thomas, A. J., Petridis, M., Walters, S. D., Gheytassi, S. M., & Morgan, R. E. (2017). Two hidden layers are usually better than one. In Engineering Applications of Neural Networks: 18th International Conference, EANN 2017, Athens, Greece, August 25–27, 2017, Proceedings (pp. 279-290). Springer International Publishing.
Uysal, M., & El Roubi, M. S. (1999). Artificial neural networks versus multiple regression in tourism demand analysis. Journal of Travel Research, 38(2), 111-118.
Yenişehirlioğlu, E., Taşar, İ., & Bayat, T. (2020). Tourism Revenue and Economic Growth Relation in Türkiye: Evidence of Symmetrical, Asymmetrical and the Rolling Window Regressions. Journal of Economic Cooperation & Development, 41(2).
Wong, K. K. F., Song, H., & Chon, K. K. S. (2006). Bayesian models for tourism demand forecasting. Tourism Management, 27(5), 773-780.
World Travel & Tourism Council (WTTC). (2023). Türkiye (Turkey) travel & tourism economic impact factsheet. Retrieved from https://cdn.prod.website-files.com/6329bc97af73223b575983ac/66695b1694d436e5830238da_Turkiye2024_.pdf
World Travel & Tourism Council (WTTC). (2024). Travel & tourism set to break all records in 2024. Retrieved from https://wttc.org/news-article/travel-and-tourism-set-to-break-all-records-in-2024-reveals-wttc
Zorlutuna, Ş., & Bircan, H. (2019). Türkiye’ye gelen turist sayısı tahmininde zaman serileri analizi ve yapay sinir ağları yöntemlerinin karşılaştırılması [Comparison of time series analysis and artificial neural network methods in forecasting the number of tourists visiting Türkiye]. Cumhuriyet Üniversitesi İktisadi ve İdari Bilimler Dergisi, 20(2), 164–185.

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Details

Primary Language	English
Subjects	Econometric and Statistical Methods, Economic Models and Forecasting, Time-Series Analysis
Journal Section	Makaleler
Authors	Begüm Kara Gülay 0000-0003-2926-2699
Early Pub Date	December 27, 2024
Publication Date	December 30, 2024
Submission Date	November 7, 2024
Acceptance Date	December 6, 2024
Published in Issue	Year 2024 Issue: 69

Cite

APA	Kara Gülay, B. (2024). MODELING AND FORECASTING OF TOURISM INCOME: THE CASE OF TURKEY. Erciyes Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi(69), 251-257. https://doi.org/10.18070/erciyesiibd.1581119

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