提出新型时空汉明距离，高效识别状态转移事件序列中的模式。
A Selective Temporal Hamming distance to find patterns in state transition event timeseries, at scale
- 定义状态转移事件时间序列，融合转移时刻与停留时长信息
- 相比传统方法，精度更高且计算更快，支持多目标状态聚焦
- 适合大规模工业、经济等复杂系统的时间序列分析
离散事件系统广泛存在于自然观察、社会经济科学及工业系统中。现有分析方法通常忽略其事件与状态的双重特性：要么将信号建模为转移事件序列，强调事件顺序对齐；要么将其视为分类或有序状态时间序列，需重采样，随着观测周期和事件数量增长，该操作变得昂贵且扭曲数据。本文提出状态转移事件时间序列（STE-ts）概念，并引入选择性时空汉明距离（STH），同时利用转移时间和状态停留时长，避免大规模数据库中耗时且扭曲的重采样。STH在保持高精度的同时，比重采样汉明与杰卡德度量具有更优的计算效率，且能聚焦多个关注状态。我们在模拟和真实数据集上验证了其优势。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。