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

Yunus Emre Gür; Kamil Abdullah Eşidir

Makale detayları

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

Yazarlar:

Yunus Emre Gür | Kamil Abdullah Eşidir

Yayıncı:

Alanya Alaaddin Keykubat Üniversitesi

Ana Başlıklar:

Ekonomik Modeller ve Öngörü | Zaman Serileri Analizi | Finansal Öngörü ve Modelleme

Dergi:

Alanya Akademik Bakış Vol.8 Issue 3, pp.885-908

Belge Tipi:

Makale

Yayın Tarihi:

9/2024

Belge Dili:

Türkçe

Document ID (Org No.):

2001328642

pISSN:

2651-4192

Anahtar kelimeler:

Özet:

 This study comprehensively evaluates LSTM, MLP, Random Forest, SVM, XGBoost, and linear regression models to forecast Turkeys 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.

 This study comprehensively evaluates LSTM, MLP, Random Forest, SVM, XGBoost, and linear regression models to forecast Turkeys 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.

 This study comprehensively evaluates LSTM, MLP, Random Forest, SVM, XGBoost, and linear regression models to forecast Turkeys 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.

 Bu çalışma, Türkiyenin 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 2024te 773.378.496 USD olan ithalat miktarının Mart 2025te 1.239.538.176 USDye 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.

Makale detayları

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

Yazarlar:

Yunus Emre Gür | Kamil Abdullah Eşidir

Yayıncı:

Alanya Alaaddin Keykubat Üniversitesi

Ana Başlıklar:

Ekonomik Modeller ve Öngörü | Zaman Serileri Analizi | Finansal Öngörü ve Modelleme

Dergi:

Alanya Akademik Bakış Vol.8 Issue 3, pp.885-908

Belge Tipi:

Makale

Yayın Tarihi:

9/2024

Belge Dili:

Türkçe

Document ID (Org No.):

2001328642

pISSN:

2651-4192

Scrap Iron and Steel Imports

LSTM

Machine Learning

Deep Learning

Ensemble Learning

LIME (Local Interpretable Model-agnostic Explanations) AnalysisHurda Demir Çelik İthalatı

LSTM

Makine Öğrenmesi

Derin Öğrenme

Topluluk Öğrenme

LIME (Local Interpretable Model-agnostic Explanations) Analizi

Sayı

Makale detayları

Yazarlar: Yunus Emre Gür | Kamil Abdullah Eşidir

Yayıncı: Alanya Alaaddin Keykubat Üniversitesi

Ana Başlıklar: Ekonomik Modeller ve Öngörü | Zaman Serileri Analizi | Finansal Öngörü ve Modelleme

Dergi: Alanya Akademik Bakış Vol.8 Issue 3, pp.885-908

Belge Tipi: Makale

Yayın Tarihi: 9/2024

Belge Dili: Türkçe

Document ID (Org No.): 2001328642

pISSN: 2651-4192

Sayı

Yazarlar:

Yunus Emre Gür | Kamil Abdullah Eşidir

Yayıncı:

Alanya Alaaddin Keykubat Üniversitesi

Ana Başlıklar:

Ekonomik Modeller ve Öngörü | Zaman Serileri Analizi | Finansal Öngörü ve Modelleme

Dergi:

Alanya Akademik Bakış Vol.8 Issue 3, pp.885-908

Belge Tipi:

Makale

Yayın Tarihi:

9/2024

Belge Dili:

Türkçe

Document ID (Org No.):

2001328642

pISSN:

2651-4192