论文标题
使用置换熵检测时间序列数据中的本地混合
Detection of Local Mixing in Time-Series Data Using Permutation Entropy
论文作者
论文摘要
尽管在实验实践中寻求尽可能高的数据速率很诱人，但如果人们对测量太密集，则过度采样可能会成为一个问题。这些效果可以采用多种形式，其中一些易于检测到：例如，当数据序列包含相同测量值的多个副本时。在其他情况下，就像在测量设备中混合$ - $时，很难检测到系统本身$ - $ $ - $ - $过采样效果。我们提出了一种新颖的，无模型的技术，可以使用称为置换熵的信息理论技术在时间序列中检测局部混合。通过改变计算的时间分辨率并分析结果中的模式，我们可以确定数据是否在本地混合，以及在什么规模上。从业人员可以使用这可以在衡量或报告数据的尺度上选择适当的下限。在几个合成示例验证了该技术之后，我们证明了它在化学实验中的数据，Mauna LOA的甲烷记录和南极冰芯上的有效性。
While it is tempting in experimental practice to seek as high a data rate as possible, oversampling can become an issue if one takes measurements too densely. These effects can take many forms, some of which are easy to detect: e.g., when the data sequence contains multiple copies of the same measured value. In other situations, as when there is mixing$-$in the measurement apparatus and/or the system itself$-$oversampling effects can be harder to detect. We propose a novel, model-free technique to detect local mixing in time series 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.
