SCALE: Support Calibration with Anchored Local Experts for Imbalanced Time-Series Classification

Rich frozen time-series representations provide many feature directions, but scarce minority labels may be insufficient to identify those that generalize. Our diagnostics reveal a gap between training and test recall for the minority class under class-balanced readouts. With the encoder held fixed, readouts trained with more labeled examples achieve higher ROC-AUC. Thus, readout performance under sparse supervision can fall short of the discrimination supported by the same frozen representations. Motivated by these observations, we propose Support Calibration with Anchored Local Experts (SCALE) to improve minority generalization without additional labels or encoder updates. SCALE uses shared local references to construct compact residual coordinates and fits complementary experts on observed and modeled support. These experts propose minority-directed corrections to a class-balanced anchor, with training cross-validation selecting the correction rule. Across 690 univariate encoder–task pairs, SCALE achieves the highest aggregate balanced accuracy, Macro-F1, and Min-F1 among the compared methods. It improves on cRT by $2.06$ balanced-accuracy and $3.43$ Min-F1 percentage points, with larger gains under more severe imbalance in a matched cohort. Multivariate evaluation also shows positive aggregate gains over cRT.