  Offline change point detection retrospectively locates change points in a
time series. Many nonparametric methods that target i.i.d. mean and variance
changes fail in the presence of nonlinear temporal dependence, and model based
methods require a known, rigid structure. For the at most one change point
problem, we propose use of a conceptor matrix to learn the characteristic
dynamics of a baseline training window with arbitrary dependence structure. The
associated echo state network acts as a featurizer of the data, and change
points are identified from the nature of the interactions between the features
and their relationship to the baseline state. This model agnostic method can
suggest potential locations of interest that warrant further study. We prove
that, under mild assumptions, the method provides a consistent estimate of the
true change point, and quantile estimates are produced via a moving block
bootstrap of the original data. The method is evaluated with clustering metrics
and Type 1 error control on simulated data, and applied to publicly available
neural data from rats experiencing bouts of non-REM sleep prior to exploration
of a radial maze. With sufficient spacing, the framework provides a simple
extension to the sparse, multiple change point problem.
