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Real-Time Change Point Detection with application to Smart Home Time Series Data
Samaneh Aminikhanghahi1, Tinghui Wang1, Diane J Cook1
1School of Electrical Engineering and Computer Science at Washington State University, Pullman, WA 99164.
IEEE Transactions on Knowledge and Data Engineering
|July 29, 2022
Summary
We introduce SEP, a new real-time algorithm for change point detection in high-dimensional time series. This method effectively identifies abrupt behavioral shifts using Separation distance, outperforming existing techniques.
Area of Science:
- Statistics
- Machine Learning
- Data Mining
Background:
- Change Point Detection (CPD) addresses identifying abrupt shifts in time series behavior.
- High-dimensional time series present unique challenges for traditional CPD methods.
Purpose of the Study:
- To introduce a novel, real-time, nonparametric algorithm for CPD in high-dimensional data.
- To evaluate the performance of the new algorithm against existing methods.
Main Methods:
- Developed the Separation-based Estimation Procedure (SEP) algorithm.
- Utilized Separation distance as a divergence measure for detecting changes.
- Tested the algorithm on both artificial and real-world datasets.
Main Results:
- The SEP algorithm demonstrated effectiveness in detecting change points in high-dimensional time series.
- Experimental results showed the proposed method's usefulness compared to existing approaches.
Conclusions:
- The SEP algorithm offers a viable new tool for real-time CPD in high-dimensional settings.
- This method shows promise for applications requiring the detection of abrupt changes in complex data streams.
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