An explainable deep learning framework for marine gas oil price forecasting: integrating LSTM with SHAP-based feature importance
Abstract Bunker fuel prices constitute a major component of maritime transport costs, representing 30–70% of total operating expenses and critically influencing competitiveness, planning, and regulatory compliance in global shipping. Their strong linkage to crude oil dynamics, coupled with additiona...
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| Asıl Yazarlar: | , |
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| Materyal Türü: | Artigo |
| Dil: | Inglês |
| Baskı/Yayın Bilgisi: |
SpringerOpen
2026-05-01
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| Seri Bilgileri: | Journal of Shipping and Trade |
| Konular: | |
| Online Erişim: | https://doi.org/10.1186/s41072-026-00237-3 |
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