Abstract
Turizm ülkelere sağladığı döviz getirisi, istihdam yaratıcı etkisi, cari açığı kapatması gibi özellikleri nedeniyle ülke ekonomileri için önemli bir sektördür. Ancak turizm talebinin ulusal ve uluslararası ekonomik, politik ve sosyal krizlerden kolaylıkla etkilenen yapısı nedeniyle, turizm talebi esnek bir yapı göstermektedir. Turizm arzının kısa ve orta vadede arttırılamaması ve altyapı yatırımlarına büyük oranda bağımlı olması nedeniyle, turizm yatırımları gerçekleştirilirken, talep yapısının tespit edilmesi ve uygun stratejilerin geliştirilmesi önemlidir. Bu durum ancak başarılı bir talep tahminiyle mümkündür. Başarılı bir talep tahmini için kullanılacak tek bir yöntemden bahsetmek mümkün değildir. Kullanılacak yöntem, veri setinin büyüklüğüne ve özelliklerine göre farklılık gösterebilmektedir. Bu nedenle talep tahmini yapılırken, birden fazla model kurulmalı ve en düşük hata oranına sahip model seçilmelidir. Gerçekleştirilen bu çalışmada, 2002 Ocak-2023 Ağustos döneminde Türkiye’ye gelen ve Bakanlık belgeli tesislerde konaklayan aylık yabancı turist verileri kullanılarak, en başarılı sonuç veren tahmin modeli oluşturulmaya çalışılmıştır. Bu kapsamda veriler, önce trend ve mevsimsel bileşen açısından incelenmiş, daha sonra Naive III, basit hareketli ortalama, çift hareketli ortalama, mevsimsel üstel düzleştirme ve yapay sinir ağları kullanılarak modellenmiştir. Oluşturulan modellerin ürettiği veriler, son 24 ayın gerçekleşen verileri ile karşılaştırılmış, MAPE ve RMSE sonuçları üzerinden değerlendirilmiştir. Araştırma sonucunda, gerçeğe en yakın sonuçları yapay sinir ağlarının ürettiği tespit edilmiştir.
References
- Akgül, I. (1994). Time Series Analysis and Forecasting Models. Recommendation: Marmara University Social Sciences Institute Journal, 1(1), 52–69.
- Atlas, M. (2013). “Time Series Analysis”, in Statistics II, Şıklar, E.; Özdemir, A. (Ed.), Eskişehir, Anadolu University Publication No: 2806. E-ISBN 978-975-06-3457-4
- Barry, K., & O’Hagan, J. (1972). An Econometric Study of British Tourist Expenditure in Ireland. Economic and Social Review, 3(2), 143–161.
- Baylar, A., Emiroğlu, E., & Arslan, A. (1999). Finding the Ratio of Swab That Will Direct to the Lateral Water Intake Structure Using Backpropagation Artificial Neural Network. Dokuz Eylül University Journal of Engineering Sciences, 1(2), 1–12.
- Bayram, O. (2018). Estimation of the Tourism Demand Equation for Turkey: Economic, Social and Political Factors [Master's Thesis]. Izmir Katip Çelebi University.
- Can, M. (2009). Forecasting in Business Using Time Series Analysis, Istanbul University, Institute of Social Sciences, Department of Business Administration, Doctoral Thesis.
- Cruz-Milan, O. (2018). Plog's Model of Personality-Based Psychographic Traits in Tourism: A Review of Empirical Research. In Tourism Planning and Destination Marketing (pp. 49–74). ISBN: 978-1-78756-292-9, eISBN: 978-1-78756-291-2
- Çağlar, T. (2007). Methods Used in Demand Forecasting and Its Application in a Fencing Wire Production Enterprise, Kırıkkale University, Institute of Science and Technology, Department of Industrial Engineering, Master's Thesis.
- Çalışkan, E., & Acar, H. H. (2006). An Evaluation on the Use of Artificial Intelligence Techniques in Wood Raw Material Production. Artvin Faculty of Forestry Journal, 7(1), 51–59.
- Çayıroğlu, I. (2019). Advanced Algorithm Analysis: 5 Artificial Neural Networks. Karabük University Faculty of Engineering. http://www.ibrahimcayiroglu.com/Dokumanlar/IleriAlgoritmaAnalizi/IleriAlgoritmaAnalizi-5.Hafta-YapaySinirAglari.pdf
- Dinler, Z. (2006). Introduction to Economics (12th ed.). Ekin Bookstore Publications.
- Edgell, D. L. (1993). World Tourism at the Millennium: An Agenda for Industry, Government, and Education. BASE. Department of Commerce, U.S. Travel and Tourism Administration.
- Göktas, L.S. (2023). The effects of Kahramanmaraş-Centered Earthquakes on the Tourism Sector and Their Response After the Earthquake Recommendations on the Steps Needed. Journal of Gastronomy, Hospitality and Travel, 6(2), 624-635.
- Hanke, J. E., Reitsch, A. G. (1992), Business Forecasting (4th Ed.), Boston, Allyn and
- Hyndman, R. J., AThanasopoulos, G. (2018) Forecasting: Principles and Practice (2nd Edition), (Online) Monash University, Australia. https://otexts.com/fpp2/
- Köroğlu, A., & Güleç, B. (2008). Youth Tourism. In N. Hacıoğlu & C. Avcıkurt (Eds.), Touristic Product Diversification. Nobel Publishing Distribution.
- Krajewski, L. J., Ritzman, L. P., Malhotra, M. K. (2010). Operations Management: Processes And Supply Chains. Upper Saddle River, New Jersey: Pearson. ISBN-13: 9780136860631
- Lewis, C. D. (1982). Industrial and Business Forecasting Methods, London, Butterworths Publishing. https://doi.org/10.1002/for.3980020210
- Lim, C. (1997). Review of International Tourism Demand Models. Annals of Tourism Research, 24(4), 835–849. https://doi.org/10.1016/s0160-7383(97)00049-2
Abstract
The structure of tourist demand is sensitive since it is very easily affected by the consequences of economic, political, and social crises. Since the limited ability to increase tourism supply, it is crucial to analyze the demand structure and develop suitable strategies. This outcome can only be achieved by an accurate and effective demand prediction. There is no singular approach that ensures success in demand forecasting. Hence, in order to estimate demand accurately, it is advisable to create many models and choose the one with the lowest error rate. This study aimed to develop the best-performing prediction model by using monthly data of international visitors who visited Türkiye from January 2002 to August 2023 and stayed in Tourism Ministry-certified accommodations. Within this framework, the data was first analyzed to identify the trend and seasonal component. Afterwards, various models were employed including Naive III, simple moving average, double moving average, seasonal exponential smoothing, and artificial neural networks. The data generated by these models has been analyzed by comparing it with the actual data from the last 24 months, using MAPE and RMSE results. According to the research findings, it has been determined that artificial neural networks produce the most accurate results.
References
- Akgül, I. (1994). Time Series Analysis and Forecasting Models. Recommendation: Marmara University Social Sciences Institute Journal, 1(1), 52–69.
- Atlas, M. (2013). “Time Series Analysis”, in Statistics II, Şıklar, E.; Özdemir, A. (Ed.), Eskişehir, Anadolu University Publication No: 2806. E-ISBN 978-975-06-3457-4
- Barry, K., & O’Hagan, J. (1972). An Econometric Study of British Tourist Expenditure in Ireland. Economic and Social Review, 3(2), 143–161.
- Baylar, A., Emiroğlu, E., & Arslan, A. (1999). Finding the Ratio of Swab That Will Direct to the Lateral Water Intake Structure Using Backpropagation Artificial Neural Network. Dokuz Eylül University Journal of Engineering Sciences, 1(2), 1–12.
- Bayram, O. (2018). Estimation of the Tourism Demand Equation for Turkey: Economic, Social and Political Factors [Master's Thesis]. Izmir Katip Çelebi University.
- Can, M. (2009). Forecasting in Business Using Time Series Analysis, Istanbul University, Institute of Social Sciences, Department of Business Administration, Doctoral Thesis.
- Cruz-Milan, O. (2018). Plog's Model of Personality-Based Psychographic Traits in Tourism: A Review of Empirical Research. In Tourism Planning and Destination Marketing (pp. 49–74). ISBN: 978-1-78756-292-9, eISBN: 978-1-78756-291-2
- Çağlar, T. (2007). Methods Used in Demand Forecasting and Its Application in a Fencing Wire Production Enterprise, Kırıkkale University, Institute of Science and Technology, Department of Industrial Engineering, Master's Thesis.
- Çalışkan, E., & Acar, H. H. (2006). An Evaluation on the Use of Artificial Intelligence Techniques in Wood Raw Material Production. Artvin Faculty of Forestry Journal, 7(1), 51–59.
- Çayıroğlu, I. (2019). Advanced Algorithm Analysis: 5 Artificial Neural Networks. Karabük University Faculty of Engineering. http://www.ibrahimcayiroglu.com/Dokumanlar/IleriAlgoritmaAnalizi/IleriAlgoritmaAnalizi-5.Hafta-YapaySinirAglari.pdf
- Dinler, Z. (2006). Introduction to Economics (12th ed.). Ekin Bookstore Publications.
- Edgell, D. L. (1993). World Tourism at the Millennium: An Agenda for Industry, Government, and Education. BASE. Department of Commerce, U.S. Travel and Tourism Administration.
- Göktas, L.S. (2023). The effects of Kahramanmaraş-Centered Earthquakes on the Tourism Sector and Their Response After the Earthquake Recommendations on the Steps Needed. Journal of Gastronomy, Hospitality and Travel, 6(2), 624-635.
- Hanke, J. E., Reitsch, A. G. (1992), Business Forecasting (4th Ed.), Boston, Allyn and
- Hyndman, R. J., AThanasopoulos, G. (2018) Forecasting: Principles and Practice (2nd Edition), (Online) Monash University, Australia. https://otexts.com/fpp2/
- Köroğlu, A., & Güleç, B. (2008). Youth Tourism. In N. Hacıoğlu & C. Avcıkurt (Eds.), Touristic Product Diversification. Nobel Publishing Distribution.
- Krajewski, L. J., Ritzman, L. P., Malhotra, M. K. (2010). Operations Management: Processes And Supply Chains. Upper Saddle River, New Jersey: Pearson. ISBN-13: 9780136860631
- Lewis, C. D. (1982). Industrial and Business Forecasting Methods, London, Butterworths Publishing. https://doi.org/10.1002/for.3980020210
- Lim, C. (1997). Review of International Tourism Demand Models. Annals of Tourism Research, 24(4), 835–849. https://doi.org/10.1016/s0160-7383(97)00049-2
Details
| Primary Language | English |
|---|---|
| Subjects | Tourism (Other) |
| Journal Section | Research Articles |
| Authors | |
| Early Pub Date | March 26, 2024 |
| Publication Date | March 28, 2024 |
| Submission Date | December 22, 2023 |
| Acceptance Date | January 9, 2024 |
| Published in Issue | Year 2024 Volume: 5 Issue: 1 |
