A Survey of Methods for Time Series Change Point Detection
By Samaneh Aminikhanghahi, Diane Joyce Cook. Knowledge and Information Systems, 2016
1,303 citations recorded by the source corpus.
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.
Subfields: Public Health, Environmental and Occupational Health, Transportation, Signal Processing
View Paper: https://www.semanticscholar.org/paper/03ca7769995660b62ea9c3b03f172edf6c934733
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Citations in the corpus
- Continual Learning in Practice (2019, methodology): “If the shifts are abrupt but non-transient, then the problem is usually called change-point detection (for a recent survey see [27]), with a divide between parametric methods that build a model of p S ( x ) [28] and non-parametric approaches based on estimating the ratio between the densities p S (…”
- Selective review of offline change point detection methods (2020, background): “127 Recent and important works include [43] which focuses on window-based detec128 tion algorithms.”