  The concept of homogeneity plays a critical role in statistics, both in its
applications as well as its theory. Change point analysis is a statistical tool
that aims to attain homogeneity within time series data. This is accomplished
through partitioning the time series into a number of contiguous homogeneous
segments. The applications of such techniques range from identifying chromosome
alterations to solar flare detection. In this manuscript we present a general
purpose search algorithm called cp3o that can be used to identify change points
in multivariate time series. This new search procedure can be applied with a
large class of goodness of fit measures. Additionally, a reduction in the
computational time needed to identify change points is accomplish by means of
probabilistic pruning. With mild assumptions about the goodness of fit measure
this new search algorithm is shown to generate consistent estimates for both
the number of change points and their locations, even when the number of change
points increases with the time series length.
  A change point algorithm that incorporates the cp3o search algorithm and
E-Statistics, e-cp3o, is also presented. The only distributional assumption
that the e-cp3o procedure makes is that the absolute $\alpha$th moment exists,
for some $\alpha\in(0,2)$. Due to this mild restriction, the e-cp3o procedure
can be applied to a majority of change point problems. Furthermore, even with
such a mild restriction, the e-cp3o procedure has the ability to detect any
type of distributional change within a time series. Simulation studies are used
to compare the e-cp3o procedure to other parametric and nonparametric change
point procedures, we highlight applications of e-cp3o to climate and financial
datasets.
