期刊
KNOWLEDGE AND INFORMATION SYSTEMS
卷 51, 期 2, 页码 339-367出版社
SPRINGER LONDON LTD
DOI: 10.1007/s10115-016-0987-z
关键词
Change point detection; Time series data; Segmentation; Machine learning; Data mining
资金
- Division Of Computer and Network Systems
- Direct For Computer & Info Scie & Enginr [1543656] Funding Source: National Science Foundation
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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