Nyamundanda, G,Hegarty, A,Hayes, K

2015

November

Journal Of Applied Statistics

Product partition latent variable model for multiple change-point detection in multivariate data

Published

()

PPM PPLVM dimensionality reduction multivariate Gaussian SEGMENTATION

42

2321

2334

The product partition model (PPM) is a well-established efficient statistical method for detecting multiple change points in time-evolving univariate data. In this article, we refine the PPM for the purpose of detecting multiple change points in correlated multivariate time-evolving data. Our model detects distributional changes in both the mean and covariance structures of multivariate Gaussian data by exploiting a smaller dimensional representation of correlated multiple time series. The utility of the proposed method is demonstrated through experiments on simulated and real datasets.

10.1080/02664763.2015.1029444