Slope Entropy: A New Time Series Complexity Estimator Based on Both Symbolic Patterns and Amplitude Information
Slope Entropy: A New Time Series Complexity Estimator Based on Both Symbolic Patterns and Amplitude Information
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DOI：
10.3390/e21121167
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发表时间：
2019-11-28
期刊：
影响因子：
2.7
通讯作者：
Cuesta-Frau D
中科院分区：
文献类型：
--
作者：
Cuesta-Frau D
开发新的测量和算法来量化数据系列的熵或相关概念是一项持续的努力，近年来在这方面带来了许多创新。最终目标通常是找到具有更高辨别能力、更有效、对噪声和伪影更稳健、对参数或配置的依赖性较小或任何其他可能需要的特征的新方法。在所有这些方法中，置换熵(PE)是一种时间序列的复杂性估计方法，由于它有许多优点，但缺点很少。这些缺点之一是PE忽略了时间序列的幅度信息。为了将这些信息引入到计算中，提出了一些PE算法的修改。本文提出了一种新的方法--斜率熵，它也解决了这一缺陷，但以不同的方式保持了子序列的符号表示，该方法使用了一种新的编码方法，该编码方法基于两个连续数据样本产生的斜率来保持子序列的符号表示。通过一组与PE和SampEn(SampEn)的比较实验，我们证明了SLOPEN是一种非常有前途的方法，其分类性能明显好于以往的方法。
The development of new measures and algorithms to quantify the entropy or related concepts of a data series is a continuous effort that has brought many innovations in this regard in recent years. The ultimate goal is usually to find new methods with a higher discriminating power, more efficient, more robust to noise and artifacts, less dependent on parameters or configurations, or any other possibly desirable feature. Among all these methods, Permutation Entropy (PE) is a complexity estimator for a time series that stands out due to its many strengths, with very few weaknesses. One of these weaknesses is the PE’s disregarding of time series amplitude information. Some PE algorithm modifications have been proposed in order to introduce such information into the calculations. We propose in this paper a new method, Slope Entropy (SlopEn), that also addresses this flaw but in a different way, keeping the symbolic representation of subsequences using a novel encoding method based on the slope generated by two consecutive data samples. By means of a thorough and extensive set of comparative experiments with PE and Sample Entropy (SampEn), we demonstrate that SlopEn is a very promising method with clearly a better time series classification performance than those previous methods.
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