Cilt 13 Sayı 4 (2025): Business & Management Studies: An International Journal
Makaleler

Tek değişkenli tahminde tahmin yöntemlerinin karşılaştırılması: Türkiye çimento endüstrisi örneği

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  • Selim Tüzüntürk,
  • Fatma Sert Eteman
Selim Tüzüntürk
Doç. Dr., Bursa Uludağ Üniversitesi, Bursa, Türkiye
Biyografi
Fatma Sert Eteman
Dr. Öğr. Üyesi, Munzur Üniversitesi, Tunceli, Türkiye
Biyografi

Yayınlanmış 25.12.2025

Anahtar Kelimeler

  • Cement Production, Artificial Neural Networks, Winters' Model, ARIMA, Forecasting
  • Çimento Üretimi, Yapay Sinir Ağları, Winters Modeli, ARIMA, Tahmin

Nasıl Atıf Yapılır

Tek değişkenli tahminde tahmin yöntemlerinin karşılaştırılması: Türkiye çimento endüstrisi örneği. (2025). Business & Management Studies: An International Journal, 13(4), 2259-2276. https://doi.org/10.15295/bmij.v13i4.2650

Telif Hakkı (c) 2025 Selim Tüzüntürk- Dr. Öğretim Üyesi

Creative Commons License

Bu çalışma Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License ile lisanslanmıştır.

Nasıl Atıf Yapılır

Tek değişkenli tahminde tahmin yöntemlerinin karşılaştırılması: Türkiye çimento endüstrisi örneği. (2025). Business & Management Studies: An International Journal, 13(4), 2259-2276. https://doi.org/10.15295/bmij.v13i4.2650

Öz

Bu çalışma, Türk çimento imalat sanayinde çimento üretimini tahmin etmek için bir yöntem bulmayı ve bu yöntemle çimento üretimini tahmin ederek gelecekteki üretim gelişimi hakkında bazı sonuçlar çıkarmayı amaçlamaktadır. Bu çalışmada kullanılan veri, Türk çimento sanayinin toplam aylık çimento üretimidir. Veriler, Ocak 2017'den Eylül 2024'e kadar 93 ayı kapsamaktadır. Oluşturulan modele yapay sinir ağı modelleme prosedürleri uygulanmış ve derin öğrenme araçları yardımıyla tahmin yapılmıştır. Tahminlerin performansı oldukça iyi olup, ağın genelleştirilebilir olduğu sonucuna varılmıştır. Sonuçlar, eğitilen ağdan elde edilen eşik değerleri ve ağırlıklar kullanılarak geliştirilen model ile aylık üretimlerin tahmin edilebileceğini göstermiştir. Aylık çimento tahminleri Aralık 2026'ya kadar yapılmıştır. Tek değişkenli zaman serisi verilerinin YSA'lar ile modellenmesinin başarısı, MAPE istatistikleri kullanılarak Winters ve SARIMA yöntemleri ile karşılaştırılarak doğrulanmıştır. En uygun tahmin yönteminin YSA olduğu bulunmuş ve bulgular tartışılmıştır.

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