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Parallelization of a Common Changepoint Detection Method

Research output: Contribution to journalJournal articlepeer-review

<mark>Journal publication date</mark>1/04/2020
<mark>Journal</mark>Journal of Computational and Graphical Statistics
Issue number1
Number of pages13
Pages (from-to)149-161
Publication StatusPublished
Early online date6/09/19
<mark>Original language</mark>English


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.