Permutation entropy: A natural complexity measure for time series
Permutation entropy: A natural complexity measure for time series
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DOI：
10.1103/physrevlett.88.174102
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发表时间：
2002-04-29
影响因子：
8.6
通讯作者：
Pompe, B
中科院分区：
文献类型：
--
作者：
Bandt, C;Pompe, B
我们基于相邻值的比较引入时间序列的复杂性参数。该定义直接适用于任意现实世界数据。对于一些众所周知的混沌动力系统，研究表明我们的复杂性与李亚普诺夫指数相似，并且在存在动力或观测噪声的情况下特别有用。我们的方法的优点是简单、计算速度极快、鲁棒性以及非线性单调变换的不变性。
We introduce complexity parameters for time series based on comparison of neighboring values. The definition directly applies to arbitrary real-world data. For some well-known chaotic dynamical systems it is shown that our complexity behaves similar to Lyapunov exponents, and is particularly useful in the presence of dynamical or observational noise. The advantages of our method are its simplicity, extremely fast calculation, robustness, and invariance with respect to nonlinear monotonous transformations.