CAPMix通过智能注入异常模式，提升复杂环境下指标异常检测的准确性。
CAPMix: Robust KPI Anomaly Detection for AIOps in Noisy and Dynamic Environments
- 引入先验引导的异常增强策略，模拟真实故障行为
- 在多个基准上优于现有方法，降低误报率
- 适合运维监控系统部署，尤其适用于噪声多变环境
时间序列异常检测对保障大规模服务可靠性至关重要。生产环境中关键性能指标（KPI）流具有高维、非平稳特性，受噪声、发布变更和隐性异常影响，真实故障与正常波动难以区分。现有方法多依赖历史正常数据或人工注入异常进行训练，但注入模式常与真实故障不符，导致决策边界偏移——即异常漂移问题。本文提出CAPMix，一种可控的异常增强框架，结合标签修正与双空间混合策略，在污染与混合数据下提升鲁棒性。CAPMix在公开AIOps与时间序列基准上持续领先，已在快手大规模生产系统部署，显著减少误报，提升监控可靠性。同时发布一个真实世界数据集，推动鲁棒KPI异常检测研究。
原文摘要 · Abstract (English)
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.
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