论文标题
多变量（可能是高维数据序列）的结构中的一般多个更改点检测
Generalized multiple change-point detection in the structure of multivariate, possibly high-dimensional, data sequences
论文作者
论文摘要
大数据技术的广泛出现导致人们对变更点检测算法的发展产生了越来越多的兴趣，这些算法可以在多变量的，可能是高度的环境中表现良好。在当前的论文中，我们提出了一种新方法，以一致地估计多变量（可能是高维，嘈杂的数据序列）中多个通用变更点的数量和位置。随着样本量和给定数据序列的维度，更改点的数量被允许增加。我们的算法具有许多构成未知多元信号的单变量信号，可以处理一般的结构变化。我们专注于多元分段信号的平均向量的变化，以及任何单变量组件信号的线性趋势的变化。我们提出的算法，标记为多变量分离株检测（MID），可以在频繁地以计算快速的方式发生频繁变化的情况下进行一致的更改点检测。
The extensive emergence of big data techniques has led to an increasing interest in the development of change-point detection algorithms that can perform well in a multivariate, possibly high-dimensional setting. In the current paper, we propose a new method for the consistent estimation of the number and location of multiple generalized change-points in multivariate, possibly high-dimensional, noisy data sequences. The number of change-points is allowed to increase with the sample size and the dimensionality of the given data sequence. Having a number of univariate signals, which constitute the unknown multivariate signal, our algorithm can deal with general structural changes; we focus on changes in the mean vector of a multivariate piecewise-constant signal, as well as changes in the linear trend of any of the univariate component signals. Our proposed algorithm, labeled Multivariate Isolate-Detect (MID), allows for consistent change-point detection in the presence of frequent changes of possibly small magnitudes in a computationally fast way.
