  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).
