As effective tools for time series analyzing, a variety of information entropies have been widely applied in engineering, economic, biomedicine and other fields. In this paper, we define a new distance between finite sequence based on inversion and derive a new entropy, Fuzzy Permutation Entropy(FPE). A comparison of recognition performance for WGN, 1/f noise, periodical, or chaotic sequence and sine waves shows that FPE is valid on distinguishing deterministic signals from stochastic signals. Further contrast studying versus FE and PE shows that FPE is more sensitive to the alteration of the complexity of time series and more effective on separating different signals. Moreover, FPE is used to explore the distinction of the complexity of various traffic flows via the time headways which is simulated from the improved brake light rule model. We hope it is conducive to design Intelligent Transportation Management System.

中文翻译：

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从时间序列段之间的新颖距离得出的模糊置换熵

作为时间序列分析的有效工具，各种信息熵已广泛应用于工程，经济，生物医学等领域。在本文中，我们基于反演定义了一个有限序列之间的新距离，并导出了一个新的熵，模糊排列熵（FPE）。对WGN，1 / f噪声，周期性或混沌序列和正弦波的识别性能的比较表明，FPE在区分确定性信号和随机信号方面是有效的。与FE和PE进行的进一步对比研究表明，FPE对时间序列复杂性的变化更敏感，并且在分离不同信号时更有效。此外，FPE用于通过改进的刹车灯规则模型模拟的时间间隔探索各种交通流的复杂性。