Parallelisation of a Common Changepoint Detection Method

TL;DR

This paper proposes parallel algorithms to improve the computational efficiency of changepoint detection methods based on dynamic programming, achieving near-quadratic speedups while maintaining asymptotic accuracy guarantees.

Contribution

It introduces two parallelisation strategies for changepoint detection that significantly reduce computation time without sacrificing asymptotic correctness.

Findings

Computational cost decreases roughly quadratically with the number of cores.

Parallel methods retain asymptotic guarantees in changepoint estimation.

Speedups are substantial in practical scenarios.

Abstract

In recent years, various means of efficiently detecting changepoints in the univariate setting have been proposed, with one popular approach involving minimising a penalised cost function using dynamic programming. In some situations, these algorithms can have an expected computational cost that is linear in the number of data points; however, the worst case cost remains quadratic. We introduce two means of improving the computational performance of these methods, both based on parallelising the dynamic programming approach. We establish that parallelisation can give substantial computational improvements: in some situations the computational cost decreases roughly quadratically in the number of cores used. These parallel implementations are no longer guaranteed to find the true minimum of the penalised cost; however, we show that they retain the same asymptotic guarantees in terms of their accuracy in estimating the number and location of the changes.