Target Fidelity in Time-Series Forecast Evaluation: A Controlled Study of Forecast Combinations

Persistent recording disturbances can make forecasting benchmarks reward agreement with altered observations. Evaluating robustness therefore requires distinguishing this agreement from fidelity to the original recorded trajectory. The distinction is difficult to measure when input repair, predictor training, and forecast combination change together. We introduce a controlled protocol that applies disturbances across the forecast boundary while retaining the pre-injection future as an observed reference. Forecasting heads are trained with equal individual losses and frozen before combination fitting, allowing the same prediction to be scored against retained-source and altered-observation targets under matched repair conditions. In a post-hoc analysis with causal repair, changing only the scoring target reverses comparisons between fixed forecasts, including reversals whose directions persist across training seeds. Oracle access to retained source inputs improves source-target accuracy while increasing altered-target error. These results motivate pairing robustness claims with an explicit scoring target and the available repair information.