# Time-Series Anomaly Detection

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arXiv Oct 27, 2026
Reconstruction residuals miss a drifting sensor: CLOSURE adds a second, separately learned channel-consistency score and reaches 88.13 Aff-F1 on SMD
Multivariate time series anomaly detection has long leaned on reconstruction residuals, but a residual only certifies that a window can be reproduced, not that its channels still explain one another as they do under normal operation. CLOSURE splits recoverability and cross-channel consistency into two prediction tasks trained separately on normal data: a diffusion reconstructor conditioned on a discrete memory of normal temporal bases scores the first, and a relation operator that predicts each channel only from its peers scores the second, with the two combined after calibration on normal data. Across the five public benchmarks SMD, MSL, SMAP, SWaT and PSM, its Aff-F1 exceeds the listed methods, reaching 88.13 on SMD.
Multivariate time series anomaly detection has long leaned on reconstruction residuals, but a residual only certifies that a window can be reproduced, not that its channels still explain one another as they do under normal operation. CLOSURE splits recoverability and cross-channel consistency into two prediction tasks trained separately on normal data: a diffusion reconstructor conditioned on a discrete memory of normal temporal bases scores the first, and a relation operator that predicts each channel only from its peers scores the second, with the two combined after calibration on normal data. Across the five public benchmarks SMD, MSL, SMAP, SWaT and PSM, its Aff-F1 exceeds the listed methods, reaching 88.13 on SMD.
Tianjin University Beijing Jiaotong University Shanghai Jiao Tong University Tsinghua University City University of Hong Kong
Fan Xu（First author）
CLOSURE Time-Series Anomaly Detection Relation-Closure Operator Diffusion Model
arXiv Nov 4, 2026
A frozen graph foundation model can flag multivariate time-series anomalies: Gfm4Mtsad reaches best or second-best on most metrics across three real datasets
Multivariate time-series anomaly detection has long relied on graph detectors trained from scratch on limited target-domain data, whose learned sensor topologies are prone to spurious correlations and cannot reuse relational priors from pretrained graph models. Gfm4Mtsad wraps a frozen graph foundation model in three peripheral modules: SICA aligns temporal features with normal-connectivity priors and restores projection-lost detail, MAPS synthesizes near-boundary pseudo-anomalies through residual-preserving local dependency breaks, and TGEC re-ranks multi-granular evidence only in the upper score tail. Across MSL P-15, SMAP T-3 and SMD 1-1 with AnomalyGFM and OwlEye backbones, the adaptation reaches best or second-best results on most metrics.
Multivariate time-series anomaly detection has long relied on graph detectors trained from scratch on limited target-domain data, whose learned sensor topologies are prone to spurious correlations and cannot reuse relational priors from pretrained graph models. Gfm4Mtsad wraps a frozen graph foundation model in three peripheral modules: SICA aligns temporal features with normal-connectivity priors and restores projection-lost detail, MAPS synthesizes near-boundary pseudo-anomalies through residual-preserving local dependency breaks, and TGEC re-ranks multi-granular evidence only in the upper score tail. Across MSL P-15, SMAP T-3 and SMD 1-1 with AnomalyGFM and OwlEye backbones, the adaptation reaches best or second-best results on most metrics.
Harbin Engineering University
Zhuangyan Hu（First author）
Gfm4Mtsad Graph Foundation Models Time-Series Anomaly Detection
researchsquare.com Nov 4, 2026
CovDiff-PaAno keeps PaAno's encoder and rewrites only the scoring: multivariate anomaly AUC-ROC rises from 0.7612 to 0.7843
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.
Shenzhen University Zhongyuan University of Technology
Guan Yang（First author）
CovDiff-PaAno Time-Series Anomaly Detection Mahalanobis Memory Scoring Patch-based Representation Learning

You’re all caught up

Reconstruction residuals miss a drifting sensor: CLOSURE adds a second, separately learned channel-consistency score and reaches 88.13 Aff-F1 on SMD

A frozen graph foundation model can flag multivariate time-series anomalies: Gfm4Mtsad reaches best or second-best on most metrics across three real datasets

CovDiff-PaAno keeps PaAno's encoder and rewrites only the scoring: multivariate anomaly AUC-ROC rises from 0.7612 to 0.7843