论文标题
广义加权置换熵
Generalized Weighted Permutation Entropy
论文作者
论文摘要
这里提出了一种新型的启发式方法，用于时间序列数据分析，称为广义加权置换熵，该方法将其合并和概括超出了其原始范围两种已建立的数据分析方法：排列熵和加权排列熵。该方法引入了缩放参数，以辨别出大小波动的序数模式的障碍和复杂性。使用此缩放参数，将复杂性 - 内向因果关系平面推广到复杂性 - 内部尺度因果关系框。证明是在由随机，混乱和随机过程以及现实世界数据产生的合成系列中进行的模拟，这些模拟显示在这三维表示中会产生独特的特征。
A novel heuristic approach is proposed here for time series data analysis, dubbed Generalized weighted permutation entropy, which amalgamates and generalizes beyond their original scope two well established data analysis methods: Permutation entropy, and Weighted permutation entropy. The method introduces a scaling parameter to discern the disorder and complexity of ordinal patterns with small and large fluctuations. Using this scaling parameter, the complexity-entropy causality plane is generalized to the complexity-entropy-scale causality box. Simulations conducted on synthetic series generated by stochastic, chaotic, and random processes, as well as real world data, are shown to produce unique signatures in this three dimensional representation.
