Papers › Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent...
Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network
Ya Su; Youjian Zhao; Chenhao Niu; Rong Liu; Wei Sun; Dan Pei
Industry devices (i.e., entities) such as server machines, spacecrafts, engines, etc., are typically monitored with multivariate time series, whose anomaly detection is critical for an entity's service quality management. However, due to the complex temporal dependence and stochasticity of multivariate time series, their anomaly detection remains a big challenge. This paper proposes OmniAnomaly, a stochastic recurrent neural network for multivariate time series anomaly detection that works well robustly for various devices. Its core idea is to capture the normal patterns of multivariate time series by learning their robust representations with key techniques such as stochastic variable connection and planar normalizing flow, reconstruct input data by the representations, and use the reconstruction probabilities to determine anomalies. Moreover, for a detected entity anomaly, OmniAnomaly can provide interpretations based on the reconstruction probabilities of its constituent univariate time series. The evaluation experiments are conducted on two public datasets from aerospace and a new server machine dataset (collected and released by us) from an Internet company. OmniAnomaly achieves an overall F1-Score of 0.86 in three real-world datasets, signicantly outperforming the best performing baseline method by 0.09. The interpretation accuracy for OmniAnomaly is up to 0.89.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Unsupervised Anomaly Detection | SMAP | OmniAnomaly | AUC | 98.89 | #6 of 9 | Archive leaderboard | report |
| Unsupervised Anomaly Detection | SMAP | OmniAnomaly | F1 | 87.28 | #6 of 9 | Archive leaderboard | report |
| Unsupervised Anomaly Detection | SMAP | OmniAnomaly | Precision | 81.30 | #6 of 9 | Archive leaderboard | report |
| Unsupervised Anomaly Detection | SMAP | OmniAnomaly | Recall | 94.19 | #6 of 9 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections