arXiv:2604.17616v2 Announce Type: replace Abstract: Root cause analysis (RCA) for time-series anomaly detection is critical for the reliable operation of complex real-world systems. Existing explanation methods often rely on unrealistic feature perturbations and ignore temporal and cross-feature dependencies, leading to unreliable attributions. We propose a conditional attribution framework that explains anomalies relative to contextually similar normal system states. Instead of using marginal or randomly sampled baselines, our method retrieves representative normal instances conditioned on th
The increasing complexity and scale of real-world AI systems necessitate more reliable root cause analysis for anomalies, which traditional methods struggle with, driving innovation in this area.
This development improves trust and operational reliability for AI-driven systems by providing more accurate explanations for anomalies, which is crucial for safety-critical applications and efficient system management.
Anomaly detection systems can now provide more contextually accurate and reliable attributions by considering temporal and cross-feature dependencies, moving beyond simplistic feature perturbations.
- · AI system operators
- · MLOps platforms
- · Industrial AI sectors
- · Security and fraud detection
- · Systems relying on naive anomaly attribution
- · Companies with high rates of false positives/negatives
Increased adoption of advanced anomaly detection and explanation frameworks in critical infrastructure and complex systems.
Improved regulatory confidence in autonomous systems as their explainability and reliability increase.
Reduced human oversight in system monitoring, potentially leading to faster response times and more efficient operations in AI-driven environments.
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Read at arXiv cs.LG