A Comprehensive Analysis of Resilient Multivariate Forecasting Models for Steel Plate Price Prediction

Yıl 2024, ERKEN GÖRÜNÜM, 1 - 1

Mahmud Alsaideen , Zeynep Ertem

https://doi.org/10.2339/politeknik.1438983

Öz

The global steel industry, holding paramount economic significance, is characterized by the inherent volatility of steel prices. Leveraging the reliable weekly steel plate price data from the Commodity Research Unit (CRU), this research employs sophisticated machine learning algorithms to forecast plate prices. The dataset spans from July 27, 2011, to July 5, 2023, encompassing six key predictive factors. Notably, total inventory levels exhibit the highest correlation (0.88) with plate prices, with the finished goods inventory value of heavy machinery emerging as the most influential factor. A comprehensive training regimen is undertaken for machine learning models, incorporating Prophet, XGBoost, LSTM, and GRU. Time Series Cross-Validation is implemented to maintain the temporal order of the data, and a Bayesian optimization function is employed for hyperparameter tuning. XGBoost emerges as the top-performing model, yielding the lowest Mean Squared Error (MSE) of 332.25 and Mean Absolute Error (MAE) of 14.55. Demonstrating superior predictive accuracy, with a Mean Absolute Percentage Error (MAPE) of 0.94% and a Root Mean Squared Error (RMSE) score of 18.06, XGBoost establishes itself as the most effective model in steel plate price forecasting. This outcome underscores the efficacy of advanced machine learning methodologies in navigating the complexities of steel market dynamics for enhanced predictive insights.

Anahtar Kelimeler

Machine Learning, Steel, Forecasting, Deep Learning

Çelik Levha Fiyat Tahmini İçin Esnek Çok Değişkenli Tahmin Modelleri

Öz

Büyük bir ekonomik öneme sahip olan küresel çelik endüstrisi, çelik fiyatlarındaki doğal değişkenlik ile karakterize edilmektedir. Emtia Araştırma Birimi'nin (CRU) güvenilir haftalık çelik levha fiyatı verilerinden yararlanan bu araştırma, levha fiyatlarını tahmin etmek için gelişmiş makine öğrenimi algoritmaları kullanıyor. Veri seti 27 Temmuz 2011 ile 5 Temmuz 2023 arasındaki dönemi kapsıyor ve altı temel tahmin faktörünü içeriyor. Özellikle, toplam stok seviyeleri levha fiyatlarıyla en yüksek korelasyonu (0,88) sergilerken, ağır makinelerin nihai ürün stok değeri en etkili faktör olarak ortaya çıkıyor. Makine öğrenimi modelleri için Prophet, XGBoost, LSTM ve GRU'yu içeren kapsamlı bir eğitim rejimi yürütülmektedir. Verilerin zamansal sırasını korumak için Zaman Serisi Çapraz Doğrulama uygulanır ve hiperparametre ayarı için bir Bayesian optimizasyon işlevi kullanıldı. XGBoost, 332,25 ile en düşük Ortalama Karesel Hatayı (MSE) ve 14,55 ile Ortalama Mutlak Hatayı (MAE) sağlayan en iyi performansa sahip model olarak ortaya çıkıyor. %0,94 Ortalama Mutlak Yüzde Hata (MAPE) ve 18,06 Ortalama Karekök Hata (RMSE) puanıyla üstün tahmin doğruluğu sergileyen XGBoost, çelik levha fiyat tahmininde en etkili model olarak kendisini kanıtlıyor. Bu sonuç, gelişmiş tahmine dayalı içgörüler için çelik piyasası dinamiklerinin karmaşıklıklarını yönetmede gelişmiş makine öğrenimi metodolojilerinin etkinliğini vurgulandı.

Anahtar Kelimeler

Makina öğrenmesi, Çelik, Derin öğrenme, Tahmin bilimi

Kaynakça

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