Abstract
<jats:p>Multivariate time-series anomaly detection is important for server monitoring, industrial systems, and intelligent operation and maintenance. Existing point-wise and point-adjusted metrics often fail to show whether an anomalous segment is detected early enough for intervention. This paper proposes a lightweight dual-score enhancement for Anomaly Transformer. The method retains the original multiplicative anomaly score and adds window-normalized reconstruction error and association discrepancy terms, so weak devi- ations near anomaly onsets become more visible without changing the backbone network or using additional labels. Experiments are conducted on four public multivariate bench- marks, namelySMD,PSM,SMAP,andMSL,underthreerandomseeds. Comparedwiththe original Anomaly Transformer score, the proposed early-warning union mode improves Raw Event Recall by 0.3435, 0.2408, 0.0995, and 0.0278 on SMD, PSM, SMAP, and MSL, respectively, and reduces mean detection delay by 53.66%, 49.97%, 52.69%, and 39.74%. Additional range- and proximity-aware metrics further show that the score-level enhance- ment improves segment coverage and early-warning behavior, while AUPRC remains dataset-dependent. The results indicate that association-discrepancy score enhancement is a practical complement to existing Transformer-based anomaly detectors when early warning is more important than point-wise matching alone.</jats:p>