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

TL;DR

This paper presents an adaptive anomaly detection framework for sequential RFS observations that learns normal behavior online, adapts to shifts, and accurately detects out-of-control point patterns using novel Power Discounting Posteriors.

Contribution

It introduces a new RFS-based adaptive detection framework with Power Discounting Posteriors for online learning and change adaptation in point pattern data.

Findings

Effective in distinguishing normal and anomalous data

Demonstrated through extensive simulations

Adapts to behavioral shifts in data

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.