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A Log-linear Time Algorithm for Constrained Changepoint Detection by Hocking, Toby Dylan; Rigaill, Guillem; Fearnhead, Paul; Bourque, Guillaume is a scholarly article available to read on EtoBox.

What is A Log-linear Time Algorithm for Constrained Changepoint Detection about?

Changepoint detection is a central problem in time series and genomic data. For some applications, it is natural to impose constraints on the directions of changes. One example is ChIP-seq data, for which adding an up-down constraint improves peak detection accuracy, but makes the optimization problem more complicated. We show how a recently proposed functional pruning technique can be adapted to solve such constrained changepoint detection problems. This leads to a new algorithm which can solve problems with arbitrary affine constraints on adjacent segment means, and which has empirical time complexity that is log-linear in the amount of data. This algorithm achieves state-of-the-art accuracy in a benchmark of several genomic data sets, and is orders of magnitude faster than existing algorithms that have similar accuracy. Our implementation is available as the PeakSegPDPA function in the coseg R package, https://github.com/tdhock/coseg

Author

Hocking, Toby Dylan; Rigaill, Guillem; Fearnhead, Paul; Bourque, Guillaume

Published

2017

Language

EN