A Selective Temporal Hamming distance to find patterns in state transition event timeseries, at scale

TL;DR

This paper introduces a novel distance metric called Selective Temporal Hamming (STH) for analyzing state transition event timeseries, which improves accuracy and efficiency by combining transition times and durations without resampling.

Contribution

The paper proposes the STH metric that generalizes existing measures, enabling scalable and precise analysis of state transition event timeseries in large datasets.

Findings

STH outperforms traditional metrics in accuracy and speed.

It effectively focuses on multiple states of interest.

Validated on both simulated and real-world data.

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.