Discrete event systems are present both in observations of nature, socio economical sciences, and industrial systems. Standard analysis approaches do not usually exploit their dual event / state nature: signals are either modeled as transition event sequences, emphasizing event order alignment, or as categorical or ordinal state timeseries, usually resampled a distorting and costly operation as the observation period and number of events grow. In this work we define state transition event timeseries (STE-ts) and propose a new Selective Temporal Hamming distance (STH) leveraging both transition time and duration-in-state, avoiding costly and distorting resampling on large databases. STH generalizes both resampled Hamming and Jaccard metrics with better precision and computation time, and an ability to focus on multiple states of interest. We validate these benefits on simulated and real-world datasets.

翻译：离散事件系统广泛存在于自然观测、社会经济科学和工业系统中。标准分析方法通常未能充分利用其事件/状态的双重特性：信号要么被建模为强调事件顺序对齐的转移事件序列，要么被建模为分类或有序状态时间序列——后者通常需要进行重采样，随着观测周期和事件数量的增加，这种操作既会造成失真又计算昂贵。本研究定义了状态转移事件时间序列（STE-ts），并提出了一种新的择时汉明距离（STH），该方法同时利用转移时间和状态持续时间，避免了对大型数据库进行昂贵且失真的重采样操作。STH将重采样汉明度量和杰卡德度量推广为统一框架，在计算精度和效率上表现更优，并能聚焦于多个关注状态。我们在仿真和真实数据集上验证了这些优势。