Papers › TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data
TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data
Shreshth Tuli, Giuliano Casale, Nicholas R. Jennings
Efficient anomaly detection and diagnosis in multivariate time-series data is of great importance for modern industrial applications. However, building a system that is able to quickly and accurately pinpoint anomalous observations is a challenging problem. This is due to the lack of anomaly labels, high data volatility and the demands of ultra-low inference times in modern applications. Despite the recent developments of deep learning approaches for anomaly detection, only a few of them can address all of these challenges. In this paper, we propose TranAD, a deep transformer network based anomaly detection and diagnosis model which uses attention-based sequence encoders to swiftly perform inference with the knowledge of the broader temporal trends in the data. TranAD uses focus score-based self-conditioning to enable robust multi-modal feature extraction and adversarial training to gain stability. Additionally, model-agnostic meta learning (MAML) allows us to train the model using limited data. Extensive empirical studies on six publicly available datasets demonstrate that TranAD can outperform state-of-the-art baseline methods in detection and diagnosis performance with data and time-efficient training. Specifically, TranAD increases F1 scores by up to 17%, reducing training times by up to 99% compared to the baselines.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Unsupervised Anomaly Detection | SMAP | TranAd | AUC | 99.21 | #3 of 9 | Archive leaderboard | report |
| Unsupervised Anomaly Detection | SMAP | TranAd | F1 | 89.15 | #3 of 9 | Archive leaderboard | report |
| Unsupervised Anomaly Detection | SMAP | TranAd | Precision | 80.43 | #3 of 9 | Archive leaderboard | report |
| Unsupervised Anomaly Detection | SMAP | TranAd | Recall | 99.99 | #3 of 9 | Archive leaderboard | report |
| Unsupervised Anomaly Detection | SMD | TranAD | Precision | 92.62 | #1 of 1 | 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.
Methods
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