Optimal detection of changepoints with a linear computational cost

Killick, Rebecca and Fearnhead, Paul and Eckley, Idris (2012) Optimal detection of changepoints with a linear computational cost. Journal of the American Statistical Association, 107 (500). pp. 1590-1598. ISSN 0162-1459

Full text not available from this repository.

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 analyse 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. This includes several established procedures for detecting changing points, such as penalised likelihood and minimum description length. 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 exact methods. We also compare with the Binary Segmentation algorithm for identifying changepoints, showing that the exactness of our approach can lead to substantial improvements in the accuracy of the inferred segmentation of the data.

Item Type:
Journal Article
Journal or Publication Title:
Journal of the American Statistical Association
Uncontrolled Keywords:
/dk/atira/pure/researchoutput/libraryofcongress/qa
Subjects:
ID Code:
60321
Deposited By:
Deposited On:
29 Nov 2012 09:56
Refereed?:
Yes
Published?:
Published
Last Modified:
25 Sep 2020 06:51