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.

翻译：离线变点检测旨在回顾性地定位时间序列中的变化点。许多针对独立同分布均值和方差变化的非参数方法在存在非线性时序依赖性的情况下失效，而基于模型的方法则需要已知的刚性结构。针对最多一个变化点问题，我们提出使用关联矩阵来学习具有任意依赖结构的基线训练窗口的特征动态。关联的储层计算网络作为数据的特征化器，通过特征间相互作用及其与基线状态关系的本质来识别变化点。这种模型无关方法可提示需要进一步研究的潜在兴趣位置。我们证明，在温和假设下，该方法提供真实变化点的一致估计，并通过原始数据的移动块自举生成分位数估计。该方法在模拟数据上使用聚类指标和第一类错误控制进行评估，并应用于探索放射迷宫前经历非快速眼动睡眠期的大鼠公开神经数据。在足够间隔条件下，该框架可简单扩展到稀疏多变化点问题。