Detecting correlation changes in multivariate time series: A comparison of four non-parametric change point detection methods
Detecting correlation changes in multivariate time series: A comparison of four non-parametric change point detection methods
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DOI：
10.3758/s13428-016-0754-9
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发表时间：
2017-06-01
影响因子：
5.4
通讯作者：
Ceulemans, Eva
中科院分区：
文献类型：
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作者：
Cabrieto, Jedelyn;Tuerlinckx, Francis;Ceulemans, Eva
多变量时间序列中的变点检测是一项复杂的任务，因为在均值旁边，当变化发生时，所监测变量的相关结构也可能改变。DeCon是最近开发的，通过结合移动窗口方法和鲁棒PCA来检测平均值和/或相关性的这种变化。然而，在文献中，已经提出了其他几种方法，采用其他非参数工具：E-分裂，多秩和KCP。由于这些方法使用不同的统计方法，需要解决两个问题。首先，应用研究人员可能会发现很难评估方法之间的差异。其次，仍然缺乏对所有这些用于捕获信号相关性变化的变化点的方法的相对性能的直接比较。因此，我们提出了DeCon，E-divisive，Multirank和KCP以及相应算法背后的基本原理，使读者更容易理解它们。我们进一步比较了他们的表现，通过广泛的模拟使用的设置Bulteel等。（生物心理学，98（1），29-42，2014）暗示的变化，在平均值和相关性结构和那些Matteson和詹姆斯（美国统计协会杂志，109（505），334-345，2014）暗示不同数量的（噪音）变量。KCP成为几乎所有环境中的最佳方法。然而，在两个以上的噪声变量的情况下，只有DeCon充分检测相关性变化。
Change point detection in multivariate time series is a complex task since next to the mean, the correlation structure of the monitored variables may also alter when change occurs. DeCon was recently developed to detect such changes in mean and\ or correlation by combining a moving windows approach and robust PCA. However, in the literature, several other methods have been proposed that employ other non-parametric tools: E-divisive, Multirank, and KCP. Since these methods use different statistical approaches, two issues need to be tackled. First, applied researchers may find it hard to appraise the differences between the methods. Second, a direct comparison of the relative performance of all these methods for capturing change points signaling correlation changes is still lacking. Therefore, we present the basic principles behind DeCon, E-divisive, Multirank, and KCP and the corresponding algorithms, to make them more accessible to readers. We further compared their performance through extensive simulations using the settings of Bulteel et al. (Biological Psychology, 98 (1), 29-42, 2014) implying changes in mean and in correlation structure and those of Matteson and James (Journal of the American Statistical Association, 109 (505), 334-345, 2014) implying different numbers of (noise) variables. KCP emerged as the best method in almost all settings. However, in case of more than two noise variables, only DeCon performed adequately in detecting correlation changes.