Real-Time Change Point Detection with Application to Smart Home Time Series Data

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 chan...

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Vydáno v:IEEE transactions on knowledge and data engineering Ročník 31; číslo 5; s. 1010 - 1023
Hlavní autoři: Aminikhanghahi, Samaneh, Wang, Tinghui, Cook, Diane J.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.05.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Activity transition detection
Algorithms
Change detection
Change detection algorithms
Detection algorithms
Dimensional changes
Divergence
Measurement
Monte Carlo simulation
Prediction algorithms
Real time
Real-time systems
separation distance
Smart buildings
Smart homes
Time series
Time series analysis
time series data
ISSN:1041-4347, 1558-2191
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Shrnutí: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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ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2018.2850347