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A Survey of Methods for Time Series Change Point Detection.
Knowledge and Information Systems ( IF 2.7 ) Pub Date : 2016-09-08 , DOI: 10.1007/s10115-016-0987-z
Samaneh Aminikhanghahi 1 , Diane J Cook 1
Affiliation  

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.

中文翻译:

时间序列变化点检测方法概述。

变更点是时间序列数据中的突然变化。这种突然的变化可能表示状态之间发生的过渡。变化点的检测对于时间序列的建模和预测很有用,并且可以在医疗状况监视,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找到。这篇调查文章列举,分类和比较了许多建议用来检测时间序列变化点的方法。所检查的方法包括已引入和评估的有监督和无监督算法。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。
更新日期:2016-09-08
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