TY  - JOUR
T1  - Çekim Modeli Çerçevesinde Ticaret Tahmininde Makine Öğrenmesi Yöntemlerinin Performans Karşılaştırması: Türkiye ve Türk Cumhuriyetleri Örneği
TT  - Performance Comparison of Machine Learning Methods in Trade Forecasting within the Framework of the Gravity Model: The Case of Turkey and the Turkic Republics
AU  - Ay, Mustafa
AU  - Ay, Ahmet
AU  - Soydal, Haldun
PY  - 2024
DA  - September
Y2  - 2024
DO  - 10.18657/yonveek.1520642
JF  - Yönetim ve Ekonomi Dergisi
JO  - YÖNEKO
PB  - Manisa Celal Bayar Üniversitesi
WT  - DergiPark
SN  - 1302-0064
SP  - 439
EP  - 459
VL  - 31
IS  - 3
LA  - tr
AB  - Bu çalışmada, Türkiye'nin Türk Cumhuriyetleri (Azerbaycan, Kazakistan, Kırgızistan, Özbekistan ve Türkmenistan) ile olan ticaret hacmi çekim modeli kullanılarak analiz edilmiş ve 2024-2025 yılları için Türkiye ile bu ülkeler arasındaki ticaret hacmini tahmin etmede en başarılı makine öğrenimi yöntemi belirlenmek istenmiştir. Bu amaçla çalışmada Türk Cumhuriyetlerinin bağımsızlıklarını kazandıkları 1992 yılından başlayarak 2023 yılına kadar olan veriler kullanılmıştır. Bu veriler sayısal değişkenler olarak Türkiye ile Türk Cumhuriyetleri arasındaki ihracat ve ithalat verileri, ülkelerin milli gelirleri, aralarındaki mesafe; kukla değişkenler olarak ise ülkelerin birbirleriyle olan sınırı, ortak dil, ülkelerin karayla çevrililik durumu ve Dünya Ticaret Örgütü (DTÖ) üyelikleridir. Bu veriler, ticaret hacmini tahmin etmek için Lineer Regresyon, Gauss Süreç Regresyonu ve Çok Katmanlı Algılayıcılar gibi farklı makine öğrenmesi modelleri ile analiz edilmiştir. Uygulanan makine öğrenmesi modellerinin başarısı MAPE (Ortalama Mutlak Yüzde Hata) değerleri üzerinden kıyaslanmıştır. Analiz sonuçları, Çok Katmanlı Algılayıcılar modelinin en doğru tahminleri sağladığını ortaya koymuştur. Bu durum, ileri düzey makine öğrenmesi yöntemlerinin karmaşık ticaret dinamiklerini anlamada ve gelecekteki ticaret eğilimlerini öngörmede ne kadar etkili olabileceğini göstermektedir. Türkiye ve Türk Cumhuriyetleri arasındaki ticaret ilişkilerinin daha iyi anlaşılması ve bu ilişkilerin gelecekteki seyrinin tahmin edilmesi, bölgesel ekonomik politikaların oluşturulmasında önemli katkılar sağlayacaktır. Çalışma, bu ülkelerle olan ticaretin gelişimine yönelik stratejilerin belirlenmesi açısından da önemlidir.Anahtar Kelimeler: Çekim Modeli, Çok Katmanlı Algılayıcılar, Gauss Süreç Regresyonu, Makine Öğrenmesi, Türk Cumhuriyetleri.JEL Sınıflandırması: C51, C53, F17
KW  - Çekim Modeli
KW  - Çok Katmanlı Algılayıcılar
KW  - Gauss Süreç Regresyonu
KW  - Makine Öğrenmesi
KW  - Türk Cumhuriyetleri
N2  - This study aims to analyze Turkey's trade volume with the Turkic Republics (Azerbaijan, Kazakhstan, Kyrgyzstan, Uzbekistan, and Turkmenistan) within the framework of the international trade gravity model and to predict the trade volume with these countries for the years 2024 and 2025. Export and import data between Turkey and the Turkic Republics from 1992 to 2023 were used, with countries' GDPs and distances as numerical variables, and border, language, landlocked status, and World Trade Organization (WTO) memberships as dummy variables. These data were processed using different machine learning models, including Linear Regression, Gaussian Process Regression, and Multilayer Perceptron, to predict trade volumes. The success of the applied machine learning models was compared based on MAPE (Mean Absolute Percentage Error) values. The analysis results indicated that the Multilayer Perceptron model provided the most accurate predictions. This finding demonstrates the effectiveness of advanced machine learning methods in understanding complex trade dynamics and forecasting future trade trends. A better understanding of trade relations between Turkey and the Turkic Republics and predicting the future trajectory of these relations will significantly contribute to the formulation of regional economic policies. The study also provides valuable insights for developing strategies to enhance trade with these countries.Key Words: Gaussian Process Regression, Gravity Model, Machine Learning, Multilayer Perceptron, Turkic Republics.JEL Classification: C51, C53, F17
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UR  - https://doi.org/10.18657/yonveek.1520642
L1  - https://dergipark.org.tr/tr/download/article-file/4089903
ER  -