TY  - JOUR
T1  - Türkiye’nin Kimyasal Madde İthalatının Gelecek Tahmini: Makine Öğrenmesi ve Topluluk Öğrenme Yöntemleri Performans Analizi
TT  - Future Forecasting of Turkey’s Chemical Imports: Performance Analysis of Machine Learning and Ensemble Learning Methods
AU  - Eşidir, Kamil Abdullah
PY  - 2025
DA  - January
Y2  - 2024
DO  - 10.18069/firatsbed.1580620
JF  - Firat University Journal of Social Sciences
PB  - Fırat Üniversitesi
WT  - DergiPark
SN  - 1300-9702
SP  - 261
EP  - 278
VL  - 35
IS  - 1
LA  - tr
AB  - 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.
KW  - Kimyasal Madde İthalatı
KW  - Makine Öğrenmesi
KW  - Topluluk Öğrenme
KW  - Ekonomik Tahmin
KW  - XGBoost
N2  - 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.
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UR  - https://doi.org/10.18069/firatsbed.1580620
L1  - http://dergipark.org.tr/tr/download/article-file/4345417
ER  -