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. 2017 May;51(2):339-367.
doi: 10.1007/s10115-016-0987-z. Epub 2016 Sep 8.

A Survey of Methods for Time Series Change Point Detection

Samaneh Aminikhanghahi  1 , Diane J Cook  1
Affiliations

A Survey of Methods for Time Series Change Point Detection

Samaneh Aminikhanghahi et al. Knowl Inf Syst. 2017 May.

Abstract

Change points are abrupt variations in time series data. Such abrupt changes may represent transitions that occur between states. Detection of change points is useful in modelling and prediction of time series and is found in application areas such as medical condition monitoring, climate change detection, speech and image analysis, and human activity analysis. This survey article enumerates, categorizes, and compares many of the methods that have been proposed to detect change points in time series. The methods examined include both supervised and unsupervised algorithms that have been introduced and evaluated. We introduce several criteria to compare the algorithms. Finally, we present some grand challenges for the community to consider.

Keywords: Change point detection; Data mining; Machine learning; Segmentation; Time series data.

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Figures

Figure 1
Figure 1
Sample time series and change points (horizontal lines indicate separate states).
Figure 2
Figure 2
An illustrative example of time series notations.
Figure 3
Figure 3
Supervised methods for change point detection.
Figure 4
Figure 4
Unsupervised methods for change point detection.
Figure 5
Figure 5
Offline vs. online CPD algorithm comparison.
See this image and copyright information in PMC

References

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