Burcu AKIN, İkbal Ece DİZBAY, Şevkinaz GÜMÜŞOĞLU, Erçin GÜDÜCÜ

YSA, DVM ve Karar Ağacı ile Tarımsal Emtiaların Fiyat Endekslerinin Tahminlenmesi: Kuru Üzüm Örneği

tahminlenmesi ekonomik aktörlere doğru alım satım kararları verebilmeleri için fayda sağlamaktadır. Türkiye’deki ticaret borsalarında işlem gören tarımsal ürünlerden biri olan kuru üzüm fiyatlarının oynak değişkenler kullanılarak tahminlenmesinin incelendiği çalışmada üç temel soru üzerinde durulmuştur. İç karışıklığın yüksek olduğu ülkelerde sosyal ve politik olaylar kuru üzüm fiyatlarını etkiler mi? Oynaklığı yüksek olan değişkenler kullanılarak kuru üzüm fiyat endeksleri tahminlenebilir mi? Son olarak, bu tip bir çalışmada Yapay Sinir Ağları (YSA), Karar Ağacı ve Destek Vektör Makineleri (DVM) yöntemlerinden hangisinin tahmin performansı daha yüksektir? Bu amaçla oluşturulan tahmin modeline YSA, KA ve DVM yöntemleri uygulanmış ve yöntemlerin tahmin performansları karşılaştırılmıştır. Uygulama sonuçları, oynak değişkenler ile sosyal ve politik olayların kuru üzüm fiyatlarının tahminlenmesinde kullanılabileceğini ve ilgili modelde DVM yönteminin en yüksek doğruluk oranını verdiğini göstermiştir.

Anahtar Kelimeler:

Emtia piyasası, Yapay sinir ağları, Karar ağacı, Destek vektör makineleri, Sosyal ve politik olaylar

Forecasting the Direction of Agricultural Commodity Price Index through ANN, SVM and Decision Tree: Evidence from Raisin

To be able to make appropriate actions during buying, selling or holding decisions, economic actors need accurate commodity price forecasts. This study focuses on forecasting raisin price by using predetermined volatile variables. Therefore, we seek for answers of three main questions. Do the social & political issues effect raisin price in countries that have internal disturbance? By using volatile variables, can we represent or predict price index thoroughly? Lastly, which method has the best prediction performance; Artificial Neural Networks (ANN), Decision Tree or Support Vector Machine (SVM)? In accordance with these purposes, ANN, decision tree and SVM methods are implemented for proposed model and their prediction performances are compared. Experimental results showed that accuracy performance of SVM method was found significantly better than ANN method and decision tree.

Keywords:

Commodity market, Artificial neural networks, Decision tree, Support vector machines, Social & political issues,

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