Expectations vs. Realities: The Cost of MSE-Optimal Forecasting Under Conditional Uncertainty

arXiv:2606.04342v1 Announce Type: new Abstract: Multi-step time series forecasting (MSF) is commonly evaluated using point-wise error metrics such as mean squared error (MSE), implicitly treating the conditional mean as a sufficient target. We show that this can be misleading under conditional uncertainty, where the conditional expectation becomes unrepresentative of typical realized values at longer horizons. We formalize this effect through a conditional uncertainty gap and prove that whenever this gap is nonzero, no deterministic predictor can simultaneously minimize MSE and match the margi

Source: arXiv cs.LG — read the full report at the original publisher.

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