Accepted manuscript
A Survey of Methods for Time Series Change Point Detection
Knowledge and information systems, Vol.51(2), pp.339-367
05/2017
Handle:
https://hdl.handle.net/2376/116572
PMCID: PMC5464762
PMID: 28603327
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.
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Details
- Title
- A Survey of Methods for Time Series Change Point Detection
- Creators
- Samaneh Aminikhanghahi - School of Electrical Engineering and Computer Science Washington State University, Pullman, WADiane J Cook - School of Electrical Engineering and Computer Science Washington State University, Pullman, WA
- Publication Details
- Knowledge and information systems, Vol.51(2), pp.339-367
- Academic Unit
- Electrical Engineering and Computer Science, School of
- Publisher
- Springer Nature; England
- Number of pages
- 29
- Grant note
- R01 EB009675 / NIBIB NIH HHS R21 NR015410 / NINR NIH HHS R01 NR016732 / NINR NIH HHS
- Identifiers
- 99900547774701842
- Language
- English
- Resource Type
- Accepted manuscript