AINeutralarXiv – CS AI · 8h ago6/10
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Expectations vs. Realities: The Cost of MSE-Optimal Forecasting Under Conditional Uncertainty
A research paper reveals a fundamental trade-off in multi-step time series forecasting: models optimized for mean squared error (MSE) produce unrealistic predictions under conditional uncertainty, failing to capture actual market variability. The study demonstrates that relaxing MSE constraints by just 5% can yield 17-30% improvements in forecast realism without sacrificing practical accuracy.