🤖 AI Summary
For large-scale discrete-event systems, analyzing state-transition event time series (STE-ts) suffers from reliance on distortion-prone resampling and difficulty in jointly modeling transition timing and state dwell duration. To address this, we propose the Selective Time Hamming (STH) distance, which directly encodes event occurrence times and state residence durations without resampling. STH unifies Hamming distance and Jaccard similarity within a single metric and supports multi-state focused matching. It preserves computational efficiency while significantly improving pattern recognition accuracy. Experiments on synthetic and real-world datasets demonstrate that STH achieves an average 2.3× speedup over baseline methods and improves clustering and anomaly detection F1-scores by 12.6%.
📝 Abstract
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.