STGAD: Self-temporal generative adversarial framework with transformer attention for unsupervised multivariate time-series anomaly detection and localization

Xiao Liao, Wei Deng, Hongyue Ma and Yihan Mu

PLOS ONE, 2026, vol. 21, issue 5, 1-22

Abstract: Unsupervised anomaly detection in multivariate time series is important for maintaining the reliability of complex cyber-physical systems. However, existing methods often face practical challenges in adversarial stability, temporal dependency modeling, and anomaly-score calibration across datasets. We present STGAD, a dual-score generative-adversarial framework for anomaly detection and localization in multivariate time series. STGAD employs a WGAN-GP critic with a Transformer encoder to perform self-temporal modeling of within-window dependencies and cross-variable interactions, and uses a stochastic generator trained under adversarial supervision with sample-level proximity regularization to model normal temporal patterns. During inference, multiple generated candidates are sampled for each input window, and the minimum residual is used as a sample-matching anomaly cue. This residual-based score is fused with the critic-based score after normalization, and final anomaly decisions are produced by distribution-adaptive thresholding. Experiments on five benchmark datasets spanning server monitoring, aerospace telemetry, industrial control, and ECG signals (SMD, SMAP, MSL, SWaT, and MIT-BIH) show that STGAD achieves strong and consistent performance against representative baselines. Ablation and robustness analyses further demonstrate the effectiveness of critic-side temporal modeling, stable adversarial learning, and dual-score fusion in the proposed framework.

Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0349223 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 49223&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0349223

DOI: 10.1371/journal.pone.0349223

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().