## Greedy Gaussian Segmentation of Multivariate Time SeriesD. Hallac, P. Nystrup, and S. Boyd
Manuscript, October 2016, updated June 2017. We consider the problem of breaking a multivariate (vector) time series into
segments over which the data is well explained as independent samples
from a Gaussian distribution.
We formulate this as a covariance-regularized maximum likelihood problem,
which can be reduced to a combinatorial optimization problem of
searching over the possible breakpoints, or segment boundaries.
This problem is in general difficult to solve globally, so we propose an efficient
heuristic method that approximately solves it, and always
yields a locally optimal choice, in the sense that no change of
any one breakpoint improves the objective.
Our method, which we call |