Component-Aware Imputation Refinement for Time Series Foundation Models

Time series foundation models generally require imputing missing historical values before forecasting. Because imputers vary across series and missingness patterns, forecast quality depends on the initial imputer. We propose component-aware imputation refinement to reduce downstream forecast error and dependence on imputer choice. Starting from an existing imputation, the method applies robust STL decomposition and constructs a local trend bridge from gap boundaries, a seasonal reference from the median of observed same-phase history, and a residual reference. Window-level structural descriptors select the correction action and magnitude, while updates are restricted to originally missing positions. In paired evaluations across 18 datasets, 16 base imputers, and four zero-shot forecasters, mean sMAPE decreases by 3.70%, and the mean within-dataset-and-forecaster standard deviation across imputers decreases by 40.51%. The results indicate improved average forecast accuracy and reduced dependence on base-imputer choice within the evaluated scope.