Parallelization of a Common Changepoint Detection Method

Tickle, Sam and Eckley, Idris and Fearnhead, Paul and Haynes, Kaylea (2020) Parallelization of a Common Changepoint Detection Method. Journal of Computational and Graphical Statistics, 29 (1). pp. 149-161. ISSN 1061-8600

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Abstract

In recent years, various means of efficiently detecting changepoints have been proposed, with one popular approach involving minimizing a penalized 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 parallelizing the dynamic programming approach. We establish that parallelization 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 penalized 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. Supplementary materials for this article are available online.

Item Type:
Journal Article
Journal or Publication Title:
Journal of Computational and Graphical Statistics
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1800/1804
Subjects:
ID Code:
135101
Deposited By:
Deposited On:
16 Jul 2019 14:15
Refereed?:
Yes
Published?:
Published
Last Modified:
25 Oct 2020 06:30