Detection of local mixing in time-series data using permutation entropy
Detection of local mixing in time-series data using permutation entropy
复制标题
使用排列熵检测时间序列数据中的局部混合
DOI：
10.1103/physreve.103.022217
复制
发表时间：
2021
影响因子：
2.4
通讯作者：
Garland, Joshua
中科院分区：
文献类型：
--
作者：
Neuder, Michael;Bradley, Elizabeth;Dlugokencky, Edward;White, James W.;Garland, Joshua
在物理实验中，序列中相邻数据点的混合是一种常见但尚未被研究的效应。这可能发生在测量仪器中(例如，如果来自多个时间点的材料同时被拉入测量室)或系统本身，例如，通过同位素在冰盖中的扩散。我们提出了一种无模型技术来检测时间序列数据中的这种局部混合，该方法使用了一种称为排列熵的信息论技术。通过改变计算的时间分辨率并分析结果中的模式，我们可以确定数据是否在本地混合，以及在多大范围内混合。从业者可以利用这一点来选择衡量或报告数据的尺度的适当下限。在几个合成实例上验证了这一技术之后，我们在一个化学实验的数据、莫纳罗亚的甲烷记录和南极冰芯上展示了它的有效性。
Mixing of neighboring data points in a sequence is a common, but understudied, effect in physical experiments. This can occur in the measurement apparatus (if material from multiple time points is pulled into a measurement chamber simultaneously, for instance) or the system itself, e.g., via diffusion of isotopes in an ice sheet. We propose a model-free technique to detect this kind oflocal mixingin time-series data using an information-theoretic technique called permutation entropy. By varying the temporal resolution of the calculation and analyzing the patterns in the results, we can determine whether the data are mixed locally, and on what scale. This can be used by practitioners to choose appropriate lower bounds on scales at which to measure or report data. After validating this technique on several synthetic examples, we demonstrate its effectiveness on data from a chemistry experiment, methane records from Mauna Loa, and an Antarctic ice core.