Harnessing the power of topological data analysis to detect change points
Harnessing the power of topological data analysis to detect change points
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DOI：
10.1002/env.2612
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发表时间：
2019-12-19
期刊：
影响因子：
1.7
通讯作者：
Gel, Yulia R.
中科院分区：
文献类型：
--
作者：
Islambekov, Umar;Yuvaraj, Monisha;Gel, Yulia R.
基于新兴的拓扑数据分析工具，我们将一种新的面向几何的方法引入到变点检测框架中。关键的理论基础是，变化点可能与数据生成过程背后的几何变化相关联。虽然拓扑数据分析在变点检测中的应用潜力非常广泛，但在本文中，我们主要关注将拓扑概念与现有的非参数变点检测方法相结合。特别是，提出的新的面向几何的方法旨在提高分布区域移位位置的检测精度。我们的仿真研究表明，将拓扑数据分析与一些现有的变点检测算法相结合，可以得到一致更准确的检测结果。我们举例说明了我们的新方法应用于两个密切相关的环境时间序列数据集-贝加尔湖冰物候和北大西洋涛动指数，以研究它们估计的制度转移位置之间的可能联系。
We introduce a novel geometry-oriented methodology, based on the emerging tools of topological data analysis, into the change-point detection framework. The key rationale is that change points are likely to be associated with changes in geometry behind the data-generating process. While the applications of topological data analysis to change-point detection are potentially very broad, in this paper, we primarily focus on integrating topological concepts with the existing nonparametric methods for change-point detection. In particular, the proposed new geometry-oriented approach aims to enhance detection accuracy of distributional regime shift locations. Our simulation studies suggest that integration of topological data analysis with some existing algorithms for change-point detection leads to consistently more accurate detection results. We illustrate our new methodology in application to the two closely related environmental time series data sets-ice phenology of the Lake Baikal and the North Atlantic Oscillation indices, in a research query for a possible association between their estimated regime shift locations.