Estimation of the Relationship Between Renewable Energy and Climate Variables Using Fuzzy Parameter Hybrid Models: A Comparative Analysis of XGBoost, Random Forest, and ARIMA

Özekinci Ş., Altay Y., İlbaş İ. N.

INFUS 8th International Conference on Intelligent and Fuzzy Systems, 7 - 09 Temmuz 2026, ss.1, (Tam Metin Bildiri)

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Sayfa Sayıları: ss.1
  • İstanbul Gelişim Üniversitesi Adresli: Evet

Özet

ARIMA, Random Forest and XGBoost algorithms were employed to

forecast the closing prices of Clean Energy Exchange-Traded Funds (ETFs),

utilizing models configured using both classical and fuzzy logic. Germany-

specific meteorological variables and financial market indicators were

incorporated into the model as predictors. In this way, the predictability of clean

energy financial instruments was assessed and compared using different

forecasting models, with the aim of providing data-driven decision-support

mechanisms for decision-makers and market participants.