Future Forecasting of Turkey’s Chemical Imports: Performance Analysis of Machine Learning and Ensemble Learning Methods

Yıl 2025, Cilt: 35 Sayı: 1, 261 - 278, 24.01.2025

Kamil Abdullah Eşidir

https://doi.org/10.18069/firatsbed.1580620

Öz

This study evaluates the performance of machine learning and ensemble learning methods to predict the future values of Turkey's chemical imports. Linear Regression, Random Forest, Rational Quadratic Regression, Support Vector Machine and XGBoost models are used. Data are obtained from reliable sources such as TurkStat and CBRT. Macroeconomic variables include Turkey's Imports, Chemicals and Chemical Products Production Index, Monthly Average Dollar Exchange Rate, Manufacturing Industry Production Index, Oil Barrel Prices and Chemicals Exports. According to the analysis results, the XGBoost model has the highest accuracy and generalization ability. The model performed well in the training, test and cross-validation sets with the lowest error rates and the highest R² values. SHAP analysis reveals that Turkey Imports and Chemicals Production Index variables have the highest impact. The projections made with the XGBoost model provide important insights into the future course of Turkey's chemical imports and are critical for economic planning and trade strategies. The model's capability facilitates strategic decisions for policymakers and the business community.

Anahtar Kelimeler

Chemicals Imports, Machine Learning, Ensemble Learning, Economic Forecasting, XGBoost

Türkiye’nin Kimyasal Madde İthalatının Gelecek Tahmini: Makine Öğrenmesi ve Topluluk Öğrenme Yöntemleri Performans Analizi

Öz

Bu çalışma, Türkiye'nin kimyasal madde ithalatının gelecekteki değerlerini tahmin etmek amacıyla makine öğrenmesi ve topluluk öğrenme yöntemlerinin performansını değerlendirmektedir. Doğrusal Regresyon, Rastgele Orman, Rasyonel Kuadratik Regresyon, Destek Vektör Makinesi ve XGBoost modelleri kullanılmıştır. Veriler, TÜİK ve TCMB gibi güvenilir kaynaklardan elde edilmiştir. Makroekonomik değişkenler arasında Türkiye İthalatı, Kimyasallar ve Kimyasal Ürünler Üretim Endeksi, Aylık Ortalama Dolar Kuru, İmalat Sanayi Üretim Endeksi, Petrol Varil Fiyatları ve Kimyasal Madde İhracatı yer almaktadır. Analiz sonuçlarına göre, XGBoost modeli en yüksek doğruluk ve genelleme yeteneğine sahiptir. Model, eğitim, test ve çapraz doğrulama setlerinde en düşük hata oranları ve en yüksek R² değerleri ile başarılı performans göstermiştir. SHAP analizi, Türkiye İthalatı ve Kimyasallar Üretim Endeksi değişkenlerinin en yüksek etkiye sahip olduğunu ortaya koymuştur. XGBoost modeli ile yapılan projeksiyonlar, Türkiye'nin kimyasal madde ithalatının gelecekteki seyrine dair önemli bilgiler sunmakta, ekonomik planlama ve ticari stratejiler için kritik öneme sahiptir. Modelin yeteneği, politika yapıcılar ve iş dünyası için stratejik kararları kolaylaştırmaktadır.

Anahtar Kelimeler

Kimyasal Madde İthalatı, Makine Öğrenmesi, Topluluk Öğrenme, Ekonomik Tahmin, XGBoost

Kaynakça

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