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Entropy and optimal partition for data analysis

机译:熵和最优分区进行数据分析

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摘要

The concept of symbolic dynamics, entropy and complexity measures has been widely utilized for the analysis of measured time series. However, little attention as been devoted to investigate the effects of choosing different partitions to obtain the coarse-grained symbolic sequences. Because the theoretical concepts of generating partitions mostly fail in the case of empirical data, one commonly introduces a homogeneous partition which ensures roughly equidistributed symbols. We will show that such a choice may lead to spurious results for the estimated entropy and will not fully reveal the randomness of the sequence.
机译:符号动力学,熵和复杂性度量的概念已广泛用于分析测量的时间序列。但是,很少有注意力用于研究选择不同分区以获得粗粒度符号序列的效果。由于生成分区的理论概念在经验数据的情况下大多会失败,因此通常会引入同质分区,以确保符号分布大致相等。我们将表明,这种选择可能会导致估计的熵产生虚假结果,并且不会完全揭示序列的随机性。

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