提出两种自适应阈值方法，提升非平稳时间序列异常检测精度。
Segmented Confidence Sequences and Multi-Scale Adaptive Confidence Segments for Anomaly Detection in Nonstationary Time Series
- 基于在线统计学习与分段思想，动态调整检测阈值。
- 在晶圆制造数据集上F1分数显著优于传统百分位法。
- 适合对误报率敏感、需实时响应的工业监控场景。
随着制造、IT和基础设施监控等领域时间序列数据日益普及，异常检测需应对统计特性随时间变化的非平稳环境。传统静态阈值易因状态转移、概念漂移或多尺度变化失效。为此，本文提出并实证评估两种新型自适应阈值框架：分段置信序列（SCS）和多尺度自适应置信分段（MACS）。二者结合统计在线学习与分段原理，在局部上下文中实现灵敏适应，即使在分布演化时仍能保持误报率控制。在晶圆制造基准数据集上的实验表明，相比传统百分位和滚动分位数方法，该方法显著提升F1分数。结果证明，基于统计原理的自适应阈值可实现可靠、可解释且及时的多种真实世界异常检测。
原文摘要 · Abstract (English)
As time series data become increasingly prevalent in domains such as manufacturing, IT, and infrastructure monitoring, anomaly detection must adapt to nonstationary environments where statistical properties shift over time. Traditional static thresholds are easily rendered obsolete by regime shifts, concept drift, or multi-scale changes. To address these challenges, we introduce and empirically evaluate two novel adaptive thresholding frameworks: Segmented Confidence Sequences (SCS) and Multi-Scale Adaptive Confidence Segments (MACS). Both leverage statistical online learning and segmentation principles for local, contextually sensitive adaptation, maintaining guarantees on false alarm rates even under evolving distributions. Our experiments across Wafer Manufacturing benchmark datasets show significant F1-score improvement compared to traditional percentile and rolling quantile approaches. This work demonstrates that robust, statistically principled adaptive thresholds enable reliable, interpretable, and timely detection of diverse real-world anomalies.
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