SCOPE：STRUCTURE- AND IMPORTANCE-AWARE PROJECTION FOR TIME-SERIES IMPUTATION

Contiguous gaps corrupt the historical inputs of time-series foundation models (TSFMs), yet lower reconstruction error at missing points does not necessarily yield better forecasts. We present SCOPE, a structure- and importance-aware projection method that requires no dedicated imputation-model training. SCOPE estimates six structural targets through Gaussian-process posterior sampling and observation-based statistics, and distinguishes downstream-use importance from structural adjustment leverage. It first reconstructs trend, seasonal, and residual components, then selectively applies a constrained structural correction that preserves observations and limits adjustment magnitude. When the target model is accessible, an optional model-aware extension provides further alignment with a fallback to the model-unaware result. Across 27 public datasets, four TSFMs, and nine block-missingness configurations, model-unaware SCOPE achieves the best combined average rank against deep baselines under two training budgets and against classical reference methods. It reduces average downstream MAE by 6.97% relative to linear interpolation and 10.43% relative to STL-Kalman. Structural-fidelity and per-unit-adjustment analyses further support the complementary roles of component-wise initialization and constrained projection.