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Optimal detection of changepoints with a linear computational cost | R. Killick
; P. Fearnhead
; I.A. Eckley
; | Date: |
7 Jan 2011 | Abstract: | We consider the problem of detecting multiple changepoints in large data
sets. Our focus is on applications where the number of changepoints will
increase as we collect more data: for example in genetics as we sequence larger
regions of the genome, or in finance as we observe time-series over longer
periods. We consider the common approach of detecting changepoints through
minimising a cost function over possible numbers and locations of changepoints.
We introduce a new method for finding the minimum of such cost functions and
hence the optimal number and location of changepoints, that has a computational
cost which, under mild conditions, is linear in the number of observations.
This compares favourably with existing methods for the same problem whose
computational cost can be quadratic, or even cubic. In simulation studies we
show that our new method can be orders of magnitude faster than these
alternative methods. We also compare with Binary Segmentation, a fast but
approximate approach to detecting changepoints and show that the exactness of
our approach can lead to substantial improvements in the accuracy of the
inferred segmentation of the data. | Source: | arXiv, 1101.1438 | Services: | Forum | Review | PDF | Favorites |
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