动态检测序列点模式异常，自动适应数据变化。
Adaptive Out-of-Control Point Pattern Detection in Sequential Random Finite Set Observations
- 基于随机有限集构建自适应检测框架，实时学习正常行为。
- 提出幂折扣后验分布，有效识别点模式异常。
- 适合需要在线监控的复杂时序数据场景。
本文提出一种针对序列随机有限集（RFS）观测的新型自适应异常检测框架。该方法通过识别过程统计行为的偏离，有效区分正常（In-Control）与异常（Out-Of-Control）数据。核心贡献在于构建了一种基于RFS的创新框架，不仅能在线学习数据生成过程的正常行为，还能动态适应行为漂移，精准识别异常点模式。为此，引入一类新的RFS后验分布——幂折扣后验（Power Discounting Posteriors, PD），在应对数据系统性变化的同时，通过新型预测后验密度函数实现点模式异常检测。大量定性与定量仿真实验验证了该方法的有效性。
原文摘要 · Abstract (English)
In this work we introduce a novel adaptive anomaly detection framework specifically designed for monitoring sequential random finite set (RFS) observations. Our approach effectively distinguishes between In-Control data (normal) and Out-Of-Control data (anomalies) by detecting deviations from the expected statistical behavior of the process. The primary contributions of this study include the development of an innovative RFS-based framework that not only learns the normal behavior of the data-generating process online but also dynamically adapts to behavioral shifts to accurately identify abnormal point patterns. To achieve this, we introduce a new class of RFS-based posterior distributions, named Power Discounting Posteriors (PD), which facilitate adaptation to systematic changes in data while enabling anomaly detection of point pattern data through a novel predictive posterior density function. The effectiveness of the proposed approach is demonstrated by extensive qualitative and quantitative simulation experiments.
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