论文标题
置换詹森·香农距离：复杂时间序列分析的多功能和快速符号工具
Permutation Jensen-Shannon distance: A versatile and fast symbolic tool for complex time series analysis
论文作者
论文摘要
本文的主要动机是引入置换詹森·香农距离，这是一种符号工具，能够量化两个任意时间序列之间的相似程度。该量词源于两个概念的融合，即Jensen-Shannon Divergence和基于数据系列元素的顺序排序的编码方案。通过几种数值和实验应用来说明此顺序符号距离的多功能性和鲁棒性，以表征和区分不同的动态。获得的结果使我们能够对其在复杂时间序列分析领域的有用性保持乐观。此外，由于其简单性，低计算成本，广泛的适用性以及对异常值和人工制品的敏感性较小，因此这种有序措施可以有效地处理大量数据并有助于应对当前的大数据挑战。
The main motivation of this paper is to introduce the permutation Jensen-Shannon distance, a symbolic tool able to quantify the degree of similarity between two arbitrary time series. This quantifier results from the fusion of two concepts, the Jensen-Shannon divergence and the encoding scheme based on the sequential ordering of the elements in the data series. The versatility and robustness of this ordinal symbolic distance for characterizing and discriminating different dynamics are illustrated through several numerical and experimental applications. Results obtained allow us to be optimistic about its usefulness in the field of complex time series analysis. Moreover, thanks to its simplicity, low computational cost, wide applicability and less susceptibility to outliers and artifacts, this ordinal measure can efficiently handle large amounts of data and help to tackle the current big data challenges.
