A Bayesian changepoint methodology for high dimensional multivariate time series and space-time data: A study of structural change using remotely sensed data

TL;DR

This paper introduces a Bayesian changepoint detection method tailored for high-dimensional multivariate and space-time data, enabling efficient analysis of structural changes such as environmental impacts from remote sensing.

Contribution

The paper presents a novel Bayesian methodology that efficiently detects change points in high-dimensional space-time data using conditionally Gaussian state space models, with significant computational improvements.

Findings

Method enables analysis of large-scale space-time data.

Algorithm is substantially faster than standard approaches.

Successfully applied to assess environmental change in Gulf Plains.

Abstract

A Bayesian approach is developed to analyze change points in multivariate time series and space-time data. The methodology is used to assess the impact of extended inundation on the ecosystem of the Gulf Plains bioregion in northern Australia. The proposed approach can be implemented for dynamic mixture models that have a conditionally Gaussian state space representation. Details are given on how to efficiently implement the algorithm for a general class of multivariate time series and space-time models. This efficient implementation makes it feasible to analyze high dimensional, but of realistic size, space-time data sets because our approach can be appreciably faster, possibly millions of times, than a standard implementation in such cases.