MSE-optimal multi-step forecasters cannot match the marginal distribution of realizations under nonzero conditional uncertainty, creating a quantifiable accuracy-realism Pareto frontier across benchmarks.
arXiv preprint arXiv:2402.08373 , year=
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Validation-based selection of inference-time rollout rules for multi-output volatility forecasters yields low-cost improvements over default MIMO deployment and recovers much of ensemble benefit at lower cost.
Recursive forecasting under partial observability is an epistemic underidentification problem, with error decomposed into teacher-forcing mismatch, approximation, and provenance gaps, recast as self-induced epistemic uncertainty.
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Expectations vs. Realities: The Cost of MSE-Optimal Forecasting Under Conditional Uncertainty
MSE-optimal multi-step forecasters cannot match the marginal distribution of realizations under nonzero conditional uncertainty, creating a quantifiable accuracy-realism Pareto frontier across benchmarks.
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Deployment-Side Adaptiveness in Multi-Horizon Volatility Forecasting
Validation-based selection of inference-time rollout rules for multi-output volatility forecasters yields low-cost improvements over default MIMO deployment and recovers much of ensemble benefit at lower cost.
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Exposure Bias as Epistemic Underidentification in Recursive Forecasting
Recursive forecasting under partial observability is an epistemic underidentification problem, with error decomposed into teacher-forcing mismatch, approximation, and provenance gaps, recast as self-induced epistemic uncertainty.