Journal
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
Volume 31, Issue 5, Pages 1010-1023Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TKDE.2018.2850347
Keywords
Activity transition detection; change detection algorithms; separation distance; smart homes; time series data
Categories
Funding
- US National Science Foundation [1543656]
- National Institutes of Health [R01EB009675]
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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.
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