Change point detection (CPD) methods aim to detect abrupt changes in time-series data. Recent CPD methods have demonstrated their potential in identifying changes in underlying statistical distributions but often fail to capture complex changes in the correlation structure in time-series data. These methods also fail to generalize effectively, as even within the same time-series, different kinds of change points (CPs) may arise that are best characterized by different types of time-series perturbations. To address this issue, we propose TiVaCPD, a CPD methodology that uses a time-varying graphical lasso based method to identify changes in correlation patterns between features over time, and combines that with an aggregate Kernel Maximum Mean Discrepancy (MMD) test to identify subtle changes in the underlying statistical distributions of dynamically established time windows. We evaluate the performance of TiVaCPD in identifying and characterizing various types of CPs in time-series and show that our method outperforms current state-of-the-art CPD methods for all categories of CPs.

翻译：变化点探测方法(CPD)旨在探测时间序列数据突变的变化。最近的CPD方法显示了其在确定基本统计分布变化方面的潜力,但往往未能捕捉时间序列数据相关结构的复杂变化。这些方法也未能有效地概括,即使在同一时间序列内,也可能产生不同种类的变化点(CPC),这些变化点的特征最好以不同时间序列的扰动为特征。为解决这一问题,我们提议了TiVaCPD, 这是一种基于时间变化的图形拉索方法,该方法使用一种基于时间的图形拉索方法来识别不同特点之间的相关性模式的变化,并结合了综合的内核最大平均值差异测试,以查明动态确定的时间窗口基本统计分布的微妙变化。我们评估了TiVaCPD在确定和描述时间序列中各种类型的CPC时序时的性表现,并表明我们的方法超越了所有类别CPC目前最先进的CPD方法。