Home > Research > Publications & Outputs > Detecting Abrupt Changes in the Presence of Loc...

Electronic data

  • DeCAFS_Revision-2

    Rights statement: 12m

    Accepted author manuscript, 970 KB, PDF document

    Embargo ends: 1/01/50

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

Text available via DOI:

View graph of relations

Detecting Abrupt Changes in the Presence of Local Fluctuations and Autocorrelated Noise

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Published
<mark>Journal publication date</mark>31/12/2022
<mark>Journal</mark>Journal of the American Statistical Association
Issue number540
Volume117
Pages (from-to)2147-2162
Publication StatusPublished
Early online date18/05/21
<mark>Original language</mark>English

Abstract

Whilst there are a plethora of algorithms for detecting changes in mean in univariate time-series, almost all struggle in real applications where there is autocorrelated noise or where the mean fluctuates locally between the abrupt changes that one wishes to detect. In these cases, default implementations, which are often based on assumptions of a constant mean between changes and independent noise, can lead to substantial over-estimation of the number of changes. We propose a principled approach to detect such abrupt changes that models local fluctuations as a random walk process and autocorrelated noise via an AR(1) process. We then estimate the number and location of changepoints by minimising a penalised cost based on this model. We develop a novel and efficient dynamic programming algorithm, DeCAFS, that can solve this minimisation problem; despite the additional challenge of dependence across segments, due to the autocorrelated noise, which makes existing algorithms inapplicable. Theory and empirical results show that our approach has greater power at detecting abrupt changes than existing approaches. We apply our method to measuring gene expression levels in bacteria.