SCAN: Enhance Time Series Anomaly Detection via Multi-Scale Neighborhood-Centered Clustering
arXiv:2606.19255v1 Announce Type: new Abstract: Time series anomaly detection plays a crucial role in a wide range of real-world applications. Reconstruction-based methods have become the mainstream paradigm, but they suffer from over-generalization and under-generalization problems, which are challenging to balance. To address this, we introduce multi-scale clustering to enhance reconstruction-based methods. At the representation level, we integrate the cluster center representations of normal patterns to constrain the model to target representative normal patterns for reconstruction, prevent
The continuous advancements in AI and machine learning necessitate more robust anomaly detection methods for increasingly complex time series data, driving research into new techniques like multi-scale clustering.
Improved time series anomaly detection is crucial for maintaining system reliability, security, and operational efficiency across various industries, impacting areas from cybersecurity to predictive maintenance.
The proposed SCAN method offers a more balanced and effective approach to anomaly detection by integrating multi-scale clustering, potentially reducing previous issues of over-generalization and under-generalization in reconstruction-based models.
- · AI/ML researchers
- · Cybersecurity industry
- · Industrial IoT platforms
- · Financial services
- · Systems relying on naive anomaly detection
- · Industries with high false-positive rates
More accurate and reliable detection of anomalies in real-time systems.
Reduced operational downtime and better predictive maintenance across critical infrastructure.
Enhanced resilience against sophisticated cyber threats and improved resource allocation through optimized system performance.
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Read at arXiv cs.LG