Zhongyuan University of Technology

CovDiff-PaAno replaces cosine matching in patch-based time-series anomaly detection with regularized Mahalanobis scoring and adds raw, first-difference and second-difference views that share one frozen encoder while each keeps its own memory bank and covariance. In a ten-seed controlled comparison on TSB-AD-M, AUC-ROC rose from 0.7612 for reproduced PaAno to 0.7843 and Range-F1 from 0.4043 to 0.4384; on TSB-AD-U the corresponding scores rose from 0.8532 to 0.8626 and from 0.4822 to 0.4924. The encoder parameter count stays at about 0.397M, but inference time grows 3.47-fold and peak inference memory 10.01-fold.

CovDiff-PaAno replaces cosine matching in patch-based time-series anomaly detection with regularized Mahalanobis scoring and adds raw, first-difference and second-difference views that share one frozen encoder while each keeps its own memory bank and covariance. In a ten-seed controlled comparison on TSB-AD-M, AUC-ROC rose from 0.7612 for reproduced PaAno to 0.7843 and Range-F1 from 0.4043 to 0.4384; on TSB-AD-U the corresponding scores rose from 0.8532 to 0.8626 and from 0.4822 to 0.4924. The encoder parameter count stays at about 0.397M, but inference time grows 3.47-fold and peak inference memory 10.01-fold.