Adaptive Out-of-Control Point Pattern Detection in Sequential Random Finite Set Observations

• URL: http://arxiv.org/abs/2506.23802v1
• Date: Mon, 30 Jun 2025 12:45:44 GMT
• Title: Adaptive Out-of-Control Point Pattern Detection in Sequential Random Finite Set Observations
• Authors: Konstantinos Bourazas, Savvas Papaioannou, Panayiotis Kolios,
• Abstract summary: We introduce a novel adaptive anomaly detection framework specifically designed for monitoring sequential random finite set (RFS) observations.<n>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.
• Score: 4.022717732460524
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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