arXiv:2604.13924v3 Announce Type: replace-cross Abstract: Time-series anomaly detection (TSAD) is critical in domains such as industrial monitoring, healthcare, and cybersecurity, but it remains challenging due to rare and heterogeneous anomalies and the scarcity of labelled data. This scarcity makes unsupervised approaches predominant, yet existing methods often rely on reconstruction or forecasting, which struggle with complex data, or on embedding-based approaches that require domain-specific anomaly synthesis and fixed distance metrics. We propose ASTER, a framework that generates pseudo-a
The increasing complexity of real-world time-series data and the persistent scarcity of labeled anomaly data necessitates more robust unsupervised anomaly detection methods for critical applications.
Improved unsupervised time-series anomaly detection will enhance the reliability and efficiency of monitoring systems across vital sectors like industrial operations, healthcare, and cybersecurity, reducing human effort and improving responsiveness.
This framework offers a new approach to anomaly detection that moves beyond the limitations of current reconstruction, forecasting, and embedding-based methods by generating more realistic pseudo-anomalies.
- · Industrial monitoring sector
- · Healthcare technology providers
- · Cybersecurity firms
- · AI/ML research institutions
- · Software reliant on simplistic anomaly detection
- · Legacy monitoring systems
- · Manual anomaly review processes
Reduced operational downtime and improved system security due to more effective anomaly detection.
Increased adoption of AI in industrial automation and mission-critical infrastructure, driven by enhanced reliability.
A potential shift in regulatory frameworks demanding higher standards for automated anomaly detection in critical systems.
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Read at arXiv cs.AI