Automated Feature Engineering Based on Explainable Artificial Intelligence for Time Series Forecasting
This work presents a practical, explainability-guided pipeline for time-series forecasting that integrates automated lag engineering, XAI-based feature selection, and a lightweight, post-hoc calibration of a tree-ensemble forecaster. Rather than proposing a new forecasting paradigm, we show that FI-...
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| Автори: | , , |
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| Формат: | Artigo |
| Мова: | Inglês |
| Опубліковано: |
IEEE
2025-01-01
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| Серія: | IEEE Access |
| Предмети: | |
| Онлайн доступ: | https://ieeexplore.ieee.org/document/11271273/ |
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