Segmented Pairwise Distance for Time Series with Large Discontinuities

TL;DR

This paper introduces the segmented pairwise distance (SPD) algorithm, improving distance measurement for time series with large discontinuities, and demonstrates its effectiveness when embedded in existing algorithms through experiments.

Contribution

The paper proposes the SPD algorithm, a novel method that enhances existing distance-based algorithms for time series with large discontinuities.

Findings

SPD-embedded algorithms outperform traditional methods in distance accuracy.

Experimental results show improved Silhouette index scores.

Validated on open and proprietary datasets.

Abstract

Time series with large discontinuities are common in many scenarios. However, existing distance-based algorithms (e.g., DTW and its derivative algorithms) may perform poorly in measuring distances between these time series pairs. In this paper, we propose the segmented pairwise distance (SPD) algorithm to measure distances between time series with large discontinuities. SPD is orthogonal to distance-based algorithms and can be embedded in them. We validate advantages of SPD-embedded algorithms over corresponding distance-based ones on both open datasets and a proprietary dataset of surgical time series (of surgeons performing a temporal bone surgery in a virtual reality surgery simulator). Experimental results demonstrate that SPD-embedded algorithms outperform corresponding distance-based ones in distance measurement between time series with large discontinuities, measured by the Silhouette index (SI).