Adversarial Autoencoder for Unsupervised Time Series Anomaly Detection and Interpretation
Open accessAbstract
In many complex systems, devices are typically monitored and generating massive multivariate time series. However, due to the complex patterns and little useful labeled data, it is a great challenge to detect anomalies from these time series data. Existing methods either rely on less regularizations, or require a large number of labeled data, leading to poor accuracy in anomaly detection. To overcome the limitations, in this paper, we propose an adversarial autoencoder anomaly detection and interpretation framework named DAEMON, which performs robustly for various datasets. The key idea is to use two discriminators to adversarially train an autoencoder to learn the normal pattern of multivariate time series, and thereafter use the reconstruction error to detect anomalies. The robustness of DAEMON is guaranteed by the regularization of hidden variables and reconstructed data using the adversarial generation method. An unsupervised approach used to detect anomalies is proposed. Moreover, in order to help operators better diagnose anomalies, DAEMON provides anomaly interpretation by computing the gradients of anomalous data. An extensive empirical study on real data offers evidence that the framework is capable of outperforming state-of-the-art methods in terms of the overall F1-score and interpretation accuracy for time series anomaly detection.
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It is beneficial to introduce an adversarial-based SSL, which can further amplify the difference between normal and anomalous data, such as USAD and DAEMON.
“It is beneficial to introduce an adversarial-based SSL, which can further amplify the difference between normal and anomalous data, such as USAD [66] and DAEMON [184].”
From Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects (2024) · cites this paper as [184]
The Abstract and §2.2 identify DAEMON as an unsupervised anomaly detector that uses adversarial training to learn normal time-series patterns; the model-variant results in §4.5.2 report a benefit from adversarial regularization. The paper does not describe DAEMON as self-supervised learning (SSL) or establish that adversarial training amplifies the difference between normal and anomalous data. The cited paper is DAEMON, so this verdict does not assess USAD. (LLM, conf=0.84)
Experimental — accuracy not yet measuredAdversarial-based regularization constraints can amplify the score difference between anomalies and normal points, thereby enhancing the accuracy of anomaly identification.
“Adversarial-based regularization constraints can amplify the score difference between anomalies and normal points, thereby enhancing the accuracy of anomalies identification from another perspective, such as Minmax learning [66], [163] and adversarial learning [184].”
From Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects (2024) · cites this paper as [184]
The ablation discussion (Effect of 1D-CNN, page unavailable) says adversarial regularization improves DAEMON’s performance and helps prevent overfitting. Section 3.4 defines anomaly scores using reconstruction error, but the paper does not establish that regularization amplifies the score difference between anomalous and normal points. That proposed mechanism is unsupported. (LLM, conf=0.86)
Experimental — accuracy not yet measured
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Sorted by citations
Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects
Zhang, K., Wen, Q., Zhang, C., Cai, R., Jin, M., Liu, Y., Zhang, J. Y., Liang, Y., Pang, G., Song, D., et al. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3387317 · Cited by 295 · see citing works
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Cyber-Attack Detection and Localization for SCADA system of CPSs
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- Authors
- Chen, X., Deng, L., Zhao, Y., & Zheng, K.
- Affiliations
- University of Electronic Science and Technology of China (CN)
- Aalborg University (DK)
- Venue
- Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining
- Publisher
- ACM
- Published
- 2023-02-27
- Pages
- 267-275
- OpenAlex
- W4321485357
- Cited by
- 36
- References
- 9
- Citations by year
- 2026: 10 · 2025: 17 · 2024: 9
- FWCI
- 6.39
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM International Conference on Web Search and Data Mining (WSDM) (publishedVersion)
- VBN Forskningsportal (Aalborg Universitet) (submittedVersion)
- Funders
- National Natural Science Foundation of China (61836007, 61832017, 61972069)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
- Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
- Keywords
- time series anomaly detection, adversarial autoencoder, unsupervised anomaly detection, multivariate time series, anomaly interpretation, reconstruction error, daemons, device monitoring, gradient-based explanation
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