Research
Core Machine Learning
Aligning Time Series on Incomparable Spaces
April 13, 2021
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.
AUTHORS
Written by
Samuel Cohen
Giulia Luise
Alexander Terenin
Brandon Amos
Marc Peter Deisenroth
Publisher
AISTATS 2021
Research Topics
Core Machine Learning
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