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RAEGL: Risk-Aware Evidence-Gated Learning for Selective Contextual Routing under Temporal Shift
Contextual specialization can improve forecasting accuracy, but a correction selected on one historical interval may become unreliable under temporal distribution shift. To address this issue, we propose RAEGL, a Risk-Aware Evidence-Gated Learning framework for selective contextual forecasting. RAEG
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