A factor model approach for the joint segmentation with between-series correlation
Methodology
2018-07-18 v2
Abstract
We consider the segmentation of set of correlated time-series, the correlation being allowed to take an arbitrary form but being the same at each time-position. We show that encoding the dependency in a factor model enables us to use the dynamic programming algorithm for the inference of the breakpoints, which remains one the most efficient algorithm. 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 an application to geodesic data is presented.
Cite
@article{arxiv.1505.05660,
title = {A factor model approach for the joint segmentation with between-series correlation},
author = {Xavier Collilieux and Emilie Lebarbier and Stéphane Robin},
journal= {arXiv preprint arXiv:1505.05660},
year = {2018}
}