Normal Support Imbalance: Correcting Support-Dependent Score Bias in Multivariate Time-Series Anomaly Detection

Normal-only multivariate time-series anomaly detectors learn from nominally normal data, yet the resulting models may cover legitimate normal behaviors unevenly. We study this Normal Support Imbalance and find that weakly supported normal observations tend to receive higher raw anomaly scores, creating the potential for false-positive disparities under a shared threshold. We introduce RestoreAD, which uses Row-Uniform regularization to discourage concentrated normal-to-normal relations, retrieves normal references for both anomaly scoring and support estimation, and applies Cross-Support Normality Calibration (CSNC). CSNC aligns training-score medians across support bins while exactly preserving within-bin rankings. On the official TSB-AD-M Eval split, RestoreAD achieves the highest mean on each reported metric among the compared baselines. Diagnostic analyses show that CSNC weakens the dependence of normal anomaly scores on estimated support difficulty and narrows cross-support false-positive-rate gaps. A controlled study further shows that unequal training support can produce score and false-positive disparities even between normal regimes with symmetric representation geometry. These findings highlight normal-reference coverage as an important consideration for reliable anomaly detection.