CAPMix: Robust KPI Anomaly Detection for AIOps in Noisy and Dynamic Environments

arXiv:2509.06419v2 Announce Type: replace-cross Abstract: Time-series anomaly detection is crucial in AIOps for maintaining large-scale service reliability. In production, streams of Key Performance Indicators (KPI) are high-dimensional, non-stationary, and affected by noise, deployment changes, and latent anomalies, making real failures hard to distinguish from benign variation. Most existing methods assume either normality (learning from "normal" history) or rely on injected anomalies for training. Yet injected patterns often misalign with real failure modes, skewing decision boundaries -- aka. Anomaly Shift. We propose CAPMix, a controllable anomaly augmentation framework with prior-guided injection for realistic temporal behaviors. CAPMix combines label revision and dual-space mixup to enhance robustness under contaminated and mixed data. CAPMix consistently outperforms state-of-the-art methods on public AIOps and time-series benchmarks. It has been deployed in Kuaishou's large-scale production system, reducing false alarms and improving monitoring reliability. A real-world dataset is also released to enrich the research on robust KPI anomaly detection.

Abstract:Time-series anomaly detection is crucial in AIOps for maintaining large-scale service reliability. In production, streams of Key Performance Indicators (KPI) are high-dimensional, non-stationary, and affected by noise, deployment changes, and latent anomalies, making real failures hard to distinguish from benign variation. Most existing methods assume either normality (learning from "normal" history) or rely on injected anomalies for training. Yet injected patterns often misalign with real failure modes, skewing decision boundaries -- aka. Anomaly Shift. We propose CAPMix, a controllable anomaly augmentation framework with prior-guided injection for realistic temporal behaviors. CAPMix combines label revision and dual-space mixup to enhance robustness under contaminated and mixed data. CAPMix consistently outperforms state-of-the-art methods on public AIOps and time-series benchmarks. It has been deployed in Kuaishou's large-scale production system, reducing false alarms and improving monitoring reliability. A real-world dataset is also released to enrich the research on robust KPI anomaly detection.

Accepted for publication at the 41st IEEE/ACM International Conference on Automated Software Engineering (ASE 2026). \c{opyright} ACM, 2026. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive Version of Record will be published by ACM, this https URL

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2509.06419 [cs.LG]

(or arXiv:2509.06419v2 [cs.LG] for this version)

https://doi.org/10.1145/3832783.3834487 DOI(s) linking to related resources

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CAPMix: Robust KPI Anomaly Detection for AIOps in Noisy and Dynamic Environments

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