Segment Parameter Labelling in MCMC Mean-Shift Change Detection

TL;DR

This paper introduces a Bayesian mean-shift change point detection method that leverages segment parameter patterns through class labels with a Dirichlet process prior, improving segmentation accuracy.

Contribution

It proposes a novel Bayesian algorithm that incorporates segment class labels for better change point detection in time series.

Findings

Enhanced segmentation performance on synthetic data

Improved detection accuracy on real-world data

Effective use of parameter repetition patterns

Abstract

This work addresses the problem of segmentation in time series data with respect to a statistical parameter of interest in Bayesian models. It is common to assume that the parameters are distinct within each segment. As such, many Bayesian change point detection models do not exploit the segment parameter patterns, which can improve performance. This work proposes a Bayesian mean-shift change point detection algorithm that makes use of repetition in segment parameters, by introducing segment class labels that utilise a Dirichlet process prior. The performance of the proposed approach was assessed on both synthetic and real world data, highlighting the enhanced performance when using parameter labelling.