论文标题
随机过程的广义置换熵
A generalized permutation entropy for random processes
论文作者
论文摘要
置换熵通过数据符号量化量量量的复杂性，该数据符号量化由称为顺序模式或仅排列的等级向量组成。该时间序列分析中这种熵越来越受欢迎的原因包括（i）它在较长的排列限制的限制中收敛到基础动力学的kolmogorov-sinai熵，以及（ii）其计算与生成和临时分区的分配。但是，当允许排列的数量以其长度的长度增长时，置换熵会发散，就像通过随机过程输出时间序列时通常一样。在这封信中，我们提出了一个广泛的置换熵，该熵是随机过程有限的，包括具有观察性或动态噪声的离散时间动态系统。
Permutation entropy measures the complexity of deterministic time series via a data symbolic quantization consisting of rank vectors called ordinal patterns or just permutations. The reasons for the increasing popularity of this entropy in time series analysis include that (i) it converges to the Kolmogorov-Sinai entropy of the underlying dynamics in the limit of ever longer permutations, and (ii) its computation dispenses with generating and ad hoc partitions. However, permutation entropy diverges when the number of allowed permutations grows super-exponentially with their length, as is usually the case when time series are output by random processes. In this Letter we propose a generalized permutation entropy that is finite for random processes, including discrete-time dynamical systems with observational or dynamical noise.
