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Journals / Gazi Mühendislik Bilimleri Dergisi / 2026 / Volume: 12 Issue: 2

Explainable and Robust Oil Price Forecasting Using Hybrid Deep Learning and SHAP Interpretability: A Long-Term Analysis of WTI Prices from 1986 to 2025

Onur Polat(BINGOL UNIVERSITY)0000-0001-9313-4910
Neslihan Parlak(SAMSUN UNIVERSITY)0009-0006-4248-9504
Esra Söğüt(Gazi Üniversitesi)0000-0002-0051-2271
Eda Fendoğlu(MALATYA TURGUT OZAL UNIVERSITY)0000-0003-4092-7137
Muammer Türkoğlu(SAMSUN UNIVERSITY)0000-0002-2377-4979
Pages
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Publication Date
2026-08-31
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results show that the proposed model successfully captures the nonlinear dynamics of oil prices and offers higher accuracy compared to existing methods.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Since the Industrial Revolution, crude oil has become one of the most important fundamental determinants shaping the global economy.
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Key Findings & Data:Crude oil is an indispensable input for strategic sectors such as petrochemicals, industrial production, transportation, defense, and manufacturing.
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Conclusion & Impact:The results show that the proposed model successfully captures the nonlinear dynamics of oil prices and offers higher accuracy compared to existing methods.
🔬Design:Modeling & Simulation
📍Scope:2025 Verileri
⚙️Variables:Crude oil price forecasting · Deep learning · Time series analysis

Abstract

Since the Industrial Revolution, crude oil has become one of the most important fundamental determinants shaping the global economy. Crude oil is an indispensable input for strategic sectors such as petrochemicals, industrial production, transportation, defense, and manufacturing. Fluctuations and sudden changes in crude oil prices profoundly affect not only energy markets but also global financial stability and political decision-making processes. Therefore, accurately predicting crude oil prices is of strategic and great importance for the stability of energy markets, the sustainability of energy policies, and economic planning. In this study, a hybrid deep learning model combining convolutional neural networks (CNN), bidirectional long-short term memory (BiLSTM), and multi-head attention mechanisms was developed and analyzed using data from January 1986 to March 2025 to forecast crude oil prices. The proposed model uses multi-scale convolutional layers to capture patterns at different scales, a BiLSTM structure to learn time series dependencies, and an attention mechanism to highlight critical information. The dataset consists of West Texas Intermediate (WTI) crude oil spot prices along with macroeconomic and energy market indicators such as exchange rates, industrial production index, consumer price index, producer price indices, and global real economic activity. The model was tested using 5-fold cross-validation and obtained values of 0.0720 MAE, 0.0966 RMSE, and 0.9905 R². Furthermore, SHAP (SHapley Additive Explanations) analysis was used to determine that variables such as jet fuel, gasoline, propane, and diesel price indices contributed most significantly to the model predictions. The results show that the proposed model successfully captures the nonlinear dynamics of oil prices and offers higher accuracy compared to existing methods. The findings obtained in this study could be critical for investors, policymakers, and governments in making strategic decisions and developing profitable policies related to global crude oil prices through the implementation of the proposed model.

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Cite This Article

Polat, O., Parlak, N., Söğüt, E., Fendoğlu, E., Türkoğlu, M. (2026). Explainable and Robust Oil Price Forecasting Using Hybrid Deep Learning and SHAP Interpretability: A Long-Term Analysis of WTI Prices from 1986 to 2025. Gazi Mühendislik Bilimleri Dergisi, Volume: 12 Issue: 2. https://doi.org/10.30855/gmbd.070526A05

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