Papers › USAD: UnSupervised Anomaly Detection on Multivariate Time Series
USAD: UnSupervised Anomaly Detection on Multivariate Time Series
Julien Audibert, Pietro Michiardi, Frédéric Guyard, Sébastien Marti, Maria A. Zuluaga
The automatic supervision of IT systems is a current challenge at Orange. Given the size and complexity reached by its IT operations, the number of sensors needed to obtain measurements over time, used to infer normal and abnormal behaviors, has increased dramatically making traditional expert-based supervision methods slow or prone to errors. In this paper, we propose a fast and stable method called UnSupervised Anomaly Detection for multivariate time series (USAD) based on adversely trained autoencoders. Its autoencoder architecture makes it capable of learning in an unsupervised way. The use of adversarial training and its architecture allows it to isolate anomalies while providing fast training. We study the properties of our methods through experiments on five public datasets, thus demonstrating its robustness, training speed and high anomaly detection performance. Through a feasibility study using Orange's proprietary data we have been able to validate Orange's requirements on scalability, stability, robustness, training speed and high performance.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Anomaly Detection | SMD | USAD | F1 | 93.82 | #1 of 2 | Archive leaderboard | report |
| Anomaly Detection | SMD | USAD | Recall | 96.17 | #1 of 2 | Archive leaderboard | report |
| Anomaly Detection | SMD | USAD | precision | 93.14 | #1 of 2 | Archive leaderboard | report |
| Unsupervised Anomaly Detection | SMAP | USAD | F1 | 81.86 | #9 of 9 | Archive leaderboard | report |
| Unsupervised Anomaly Detection | SMAP | USAD | Precision | 76.97 | #9 of 9 | Archive leaderboard | report |
| Unsupervised Anomaly Detection | SMAP | USAD | Recall | 98.31 | #9 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.
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