应用于智能家居时间序列数据的实时变化点检测
Real-Time Change Point Detection with application to Smart Home Time Series Data.
作者信息
Aminikhanghahi Samaneh, Wang Tinghui, Cook Diane J
机构信息
School of Electrical Engineering and Computer Science at Washington State University, Pullman, WA 99164.
出版信息
IEEE Trans Knowl Data Eng. 2019 May;31(5):1010-1023. doi: 10.1109/tkde.2018.2850347. Epub 2018 Jun 25.
Abstract
Change Point Detection (CPD) is the problem of discovering time points at which the behavior of a time series changes abruptly. In this paper, we present a novel real-time nonparametric change point detection algorithm called SEP, which uses Separation distance as a divergence measure to detect change points in high-dimensional time series. Through experiments on artificial and real-world datasets, we demonstrate the usefulness of the proposed method in comparison with existing methods.
摘要
变化点检测（CPD）是发现时间序列行为发生突然变化的时间点的问题。在本文中，我们提出了一种名为SEP的新型实时非参数变化点检测算法，该算法使用分离距离作为散度度量来检测高维时间序列中的变化点。通过在人工数据集和真实世界数据集上的实验，我们证明了所提出的方法与现有方法相比的有效性。
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