A Survey of Methods for Time Series Change Point Detection.
A Survey of Methods for Time Series Change Point Detection.
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DOI：
10.1007/s10115-016-0987-z
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发表时间：
2017-05
影响因子：
2.7
通讯作者：
Cook DJ
中科院分区：
文献类型：
--
作者：
Aminikhanghahi S;Cook DJ
变点是时间序列数据中的突变。这样的突然变化可以表示在状态之间发生的转变。变化点的检测在时间序列的建模和预测中是有用的，并且在诸如医疗状况监测、气候变化检测、语音和图像分析以及人类活动分析等应用领域中被发现。这篇调查文章列举，分类，并比较了许多已提出的方法来检测时间序列中的变化点。检查的方法包括监督和无监督的算法，已被引入和评估。我们介绍了几个标准来比较算法。最后，我们提出了一些重大挑战，供社区考虑。
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.