• Séminaire / Formations,

Séminaire MODAL'X : Guillem Rigaill (INRAE et LaMME, Université d'Evry)

Publié le 5 février 2021 Mis à jour le 5 février 2021

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


le 18 février 2021

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Résumé :
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. We apply our method to measuring gene expression levels in bacteria.

This is a joint work with Gaetano Romano, Vincent Runge, Paul Fearnhead

Mis à jour le 05 février 2021