ICML 2017poster956 citations
Soft-DTW: a Differentiable Loss Function for Time-Series
Abstract
We propose in this paper a differentiable learning loss between time series, building upon the celebrated dynamic time warping (DTW) discrepancy. Unlike the Euclidean distance, DTW can compare time series of variable size and is robust to shifts or dilatations across the time dimension. To compute DTW, one typically solves a minimal-cost alignment problem between two time series using dynamic programming. Our work takes advantage of a smoothed formulation of DTW, called soft-DTW, that computes the soft-minimum of all alignment costs. We show in this paper that soft-DTW is a
BibTeX
@InProceedings{pmlr-v70-cuturi17a,
title = {Soft-{DTW}: a Differentiable Loss Function for Time-Series},
author = {Marco Cuturi and Mathieu Blondel},
booktitle = {Proceedings of the 34th International Conference on Machine Learning},
pages = {894--903},
year = {2017},
editor = {Precup, Doina and Teh, Yee Whye},
volume = {70},
series = {Proceedings of Machine Learning Research},
month = {06--11 Aug},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v70/cuturi17a/cuturi17a.pdf},
url = {https://proceedings.mlr.press/v70/cuturi17a.html},
abstract = {We propose in this paper a differentiable learning loss between time series, building upon the celebrated dynamic time warping (DTW) discrepancy. Unlike the Euclidean distance, DTW can compare time series of variable size and is robust to shifts or dilatations across the time dimension. To compute DTW, one typically solves a minimal-cost alignment problem between two time series using dynamic programming. Our work takes advantage of a smoothed formulation of DTW, called soft-DTW, that computes the soft-minimum of all alignment costs. We show in this paper that soft-DTW is a