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A factor model approach for the joint segmentation with between‐series correlation
Author(s) -
Collilieux Xavier,
Lebarbier Emilie,
Robin Stéphane
Publication year - 2019
Publication title -
scandinavian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.359
H-Index - 65
eISSN - 1467-9469
pISSN - 0303-6898
DOI - 10.1111/sjos.12368
Subject(s) - series (stratigraphy) , mathematics , segmentation , algorithm , inference , representation (politics) , geodesic , model selection , constant (computer programming) , set (abstract data type) , artificial intelligence , computer science , statistics , geometry , paleontology , politics , political science , law , biology , programming language
We consider the detection of changes in the mean of a set of time series. The breakpoints are allowed to be series specific, and the series are assumed to be correlated. The correlation between the series is supposed to be constant along time but is allowed to take an arbitrary form. We show that such a dependence structure can be encoded in a factor model. Thanks to this representation, the inference of the breakpoints can be achieved via dynamic programming, which remains one the most efficient algorithms. We propose a model selection procedure to determine both the number of breakpoints and the number of factors. This proposed method is implemented in the FASeg R package, which is available on the CRAN. We demonstrate the performances of our procedure through simulation experiments and present an application to geodesic data.