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).

中文翻译：

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具有大不连续性的时间序列的分段成对距离

具有大不连续性的时间序列在许多场景中都很常见。然而，现有的基于距离的算法（例如，DTW 及其衍生算法）在测量这些时间序列对之间的距离时可能表现不佳。在本文中，我们提出了分段成对距离（SPD）算法来测量具有大不连续性的时间序列之间的距离。SPD 与基于距离的算法正交，可以嵌入其中。我们在开放数据集和手术时间序列的专有数据集（外科医生在虚拟现实手术模拟器中进行颞骨手术）上验证了 SPD 嵌入式算法相对于相应的基于距离的算法的优势。