Offline change point detection seeks to identify points in a time series where the data generating process changes. This problem is well studied for univariate i.i.d. data, but becomes challenging with increasing dimension and temporal dependence. For the at most one change point problem, we propose the use of a conceptor matrix to learn the characteristic dynamics of a specified training window in a time series. The associated random recurrent neural network acts as a featurizer of the data, and change points are identified from a univariate quantification of the distance between the featurization and the space spanned by a representative conceptor matrix. 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 for statistics are produced via a moving block bootstrap of the original data. The method is tested on simulations from several classes of processes, and we evaluate performance with clustering metrics, graphical methods, and observed Type 1 error control. We apply our method to publicly available neural data from rats experiencing bouts of non-REM sleep prior to exploration of a radial maze.

翻译：离线变点检测旨在识别时间序列中数据生成过程发生变化的点。这一问题在单变量独立同分布数据中已被充分研究，但随着数据维度和时间依赖性的增加，其挑战性显著提升。针对至多存在一个变点的问题，我们提出使用概念器矩阵学习时间序列中指定训练窗口的特征动力学。关联的随机递归神经网络充当数据的特征提取器，通过单变量量化特征化结果与代表性概念器矩阵张成空间之间的距离来识别变点。这种与模型无关的方法能够提示值得进一步研究的关键位置。我们证明，在温和假设下，该方法可对真实变点提供一致估计，并通过原始数据的移动块自助法生成统计量的分位数估计。该方法在多类过程模拟中进行了测试，我们通过聚类指标、图形方法和观测到的第一类错误控制来评估性能。将所提方法应用于公开的神经数据——该数据记录了大鼠探索径向迷宫前非快速眼动睡眠发作期间的神经活动。