| Literature DB >> 35903759 |
Samaneh Aminikhanghahi1, Tinghui Wang1, Diane J Cook1.
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.Entities:
Keywords: Activity transition detection; Separation distance; change detection algorithms; smart homes; time series data
Year: 2018 PMID: 35903759 PMCID: PMC9328027 DOI: 10.1109/tkde.2018.2850347
Source DB: PubMed Journal: IEEE Trans Knowl Data Eng ISSN: 1041-4347 Impact factor: 9.235