Forecasting the Future Values of Turkey's Scrap Iron and Steel Imports with Deep Learning, Machine Learning and Ensemble Learning Methods

Yıl 2024, Cilt: 8 Sayı: 3, 885 - 908, 30.09.2024

Yunus Emre Gür , Kamil Abdullah Eşidir

https://doi.org/10.29023/alanyaakademik.1497646

Öz

This study comprehensively evaluates LSTM, MLP, Random Forest, SVM, XGBoost, and linear regression models to forecast Turkey's scrap iron and steel imports. The performance of the models is measured using RMSE, MSE, MAE, MAPE and R² metrics. The LSTM model shows the best forecasting performance, achieving RMSE 0.0387, MSE 0.0014, MAE 0.0297, MAPE 0.1261, and R² 0.9631 in the training set. According to import forecasts for the next 12 months, imports are expected to increase from 773,378,496 USD in April 2024 to 1,239,538,176 USD in March 2025. LIME analysis makes the decision-making processes of the model transparent by explaining which independent variables the model is based on. As a result of the analysis, it is determined that the model attaches great importance to variables such as “PPI” and “Monthly Iron and Steel Imports”, and the impact of these variables on the forecast results is more significant than other independent variables. With this analysis, the impact of each independent variable on the results of the model is visualized and the contribution levels of the variables are evaluated to reveal which features are given more weight by the model.

Anahtar Kelimeler

Scrap Iron and Steel Imports, LSTM, Machine Learning, Deep Learning, Ensemble Learning, LIME (Local Interpretable Model-agnostic Explanations) Analysis

Türkiye Hurda Demir Çelik İthalatının Gelecek Değerlerinin Derin Öğrenme, Makine Öğrenmesi ve Topluluk Öğrenme Yöntemleri ile Öngörülmesi

Öz

Bu çalışma, Türkiye’nin hurda demir çelik ithalatını tahmin etmek için LSTM, MLP, Random Forest, SVM, XGBoost ve Doğrusal Regresyon modellerini kapsamlı bir şekilde değerlendirmektedir. Modellerin performansları RMSE, MSE, MAE, MAPE ve R² metrikleri kullanılarak ölçülmüştür. LSTM modeli, en iyi tahmin performansını göstererek eğitim setinde RMSE 0,0387, MSE 0,0014, MAE 0,0297, MAPE 0,1261 ve R² 0.9631 sonuçlarını elde etmiştir. Gelecek 12 aylık ithalat tahminlerine göre, Nisan 2024’te 773.378.496 USD olan ithalat miktarının Mart 2025'te 1.239.538.176 USD’ye ulaşması beklenmektedir. LIME analizi, modelin hangi bağımsız değişkenlere dayandığını açıklayarak modelin karar verme süreçlerini şeffaf hale getirmektedir. Analiz sonucunda, modelin özellikle “YÜFE” ve “Aylık Demir Çelik İthalatı” gibi değişkenlere yüksek önem verdiği, bu değişkenlerin tahmin sonuçları üzerindeki etkisinin diğer bağımsız değişkenlere göre daha belirgin olduğu tespit edilmiştir. Bu analiz ile her bir bağımsız değişkenin modelin sonuçları üzerindeki etkisi görselleştirilmiş ve değişkenlerin katkı düzeyleri değerlendirilerek modelin hangi özelliklere daha fazla ağırlık verdiği ortaya konulmuştur.

Anahtar Kelimeler

Hurda Demir Çelik İthalatı, LSTM, Makine Öğrenmesi, Derin Öğrenme, Topluluk Öğrenme, LIME (Local Interpretable Model-agnostic Explanations) Analizi

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