Conference Proceedings

Segmented Pairwise Distance for Time Series with Large Discontinuities

Jiabo He, Sarah Erfani, Sudanthi Wijewickrema, Stephen O'Leary, Kotagiri Ramamohanarao

Proceedings of the International Joint Conference on Neural Networks | IEEE | Published : 2020

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). E..

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Grants

Funding Acknowledgements

We truly appreciated great cooperation with 7 anonymous surgeons at the Royal Victorian Eye and Ear Hospital, who helped us perform and validate CM surgeries in the VRTBS simulator. This research was supported by the Melbourne Research Scholarship.