Local Level Dynamic Random Partition Models for Changepoint Detection

TL;DR

This paper introduces a novel state-space model for multivariate time series that detects changepoints and clusters data dynamically, leveraging a Markov structure and false discovery rate control for improved accuracy.

Contribution

It develops a new dynamic random partition model with a simple Gibbs sampling implementation, capable of handling dependent changepoint decisions and multiview clustering scenarios.

Findings

Effective in detecting changepoints in simulated data

Successfully applied to human gesture dataset

Outperforms existing methods in dynamic clustering

Abstract

Motivated by an increasing demand for models that can effectively describe features of complex multivariate time series, e.g. from sensor data in biomechanics, motion analysis, and sports science, we introduce a novel state-space modeling framework where the state equation encodes the evolution of latent partitions of the data over time. Building on the principles of dynamic linear models, our approach develops a random partition model capable of linking data partitions to previous ones over time, using a straightforward Markov structure that accounts for temporal persistence and facilitates changepoint detection. The selection of changepoints involves multiple dependent decisions, and we address this time-dependence by adopting a non-marginal false discovery rate control. This leads to a simple decision rule that ensures more stringent control of the false discovery rate compared to approaches that do not consider dependence. The method is efficiently implemented using a Gibbs sampling algorithm, leading to a straightforward approach compared to existing methods for dependent random partition models. Additionally, we show how the proposed method can be adapted to handle multiview clustering scenarios. Simulation studies and the analysis of a human gesture phase dataset collected through various sensing technologies show the effectiveness of the method in dynamically clustering multivariate time series and detecting changepoints.