SOURCE-LINKED INTELLIGENCE
Beyond Average Error through Oracle-Informed Stress Tests for Time-Series Forecasting
Average squared error cannot reveal whether forecasting performance degrades because the future becomes less predictable or because forecasts move farther from the conditional mean. We introduce paired, mechanism-controlled stress tests that decompose changes in expected squared error at each lead time into environmental risk and forecast-oracle distance, using an origin-conditioned predictive oracle unavailable to the evaluated models. Three end-to-end controls have known attribution. Specifically, the null, environmental-only, and information-gap controls verify that the pipeline assigns cha
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-19T06:42:49.000Z
First collected: 2026-09-23T12:01:45.602Z. This is not the publication date.