Dual-Score Association-Discrepancy Enhancement for Early-Warning Multivariate Time-Series Anomaly Detection

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 deviations near anomaly onsets become more visible without changing the backbone network or using additional labels. Experiments are conducted on four public multivariate benchmarks, namely, SMD, PSM, SMAP, and MSL, under three random seeds. Compared with the 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%. Event precision, event F1, and false positive rate are also reported in order to make explicit the trade-off between recall and false alarms. Further range- and proximity-aware metrics additionally show that the score-level enhancement 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.