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Aligning Time Series on Incomparable Spaces.

Cohen, S; Luise, G; Terenin, A; Amos, B; Deisenroth, MP; (2021) Aligning Time Series on Incomparable Spaces. In: Banerjee, A and Fukumizu, K, (eds.) Proceedings of the 24th International Conference on Artificial Intelligence and Statistics. (pp. pp. 1036-1044). PMLR Green open access

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Abstract

Dynamic time warping (DTW) is a useful method for aligning, comparing and combining time series, but it requires them to live in comparable spaces. In this work, we consider a setting in which time series live on different spaces without a sensible ground metric, causing DTW to become ill-defined. To alleviate this, we propose Gromov dynamic time warping (GDTW), a distance between time series on potentially incomparable spaces that avoids the comparability requirement by instead considering intra-relational geometry. We demonstrate its effectiveness at aligning, combining and comparing time series living on incomparable spaces. We further propose a smoothed version of GDTW as a differentiable loss and assess its properties in a variety of settings, including barycentric averaging, generative modeling and imitation learning.

Type: Proceedings paper
Title: Aligning Time Series on Incomparable Spaces.
Event: International Conference on Artificial Intelligence and Statistics
Open access status: An open access version is available from UCL Discovery
Publisher version: http://proceedings.mlr.press/v130/
Language: English
Additional information: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10126869
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