Unsupervised Scalable Representation Learning for Multivariate Time Series
Jean-Yves Franceschi, Aymeric Dieuleveut, Martin Jaggi
Abstract
Time series constitute a challenging data type for machine learning algorithms, due to their highly variable lengths and sparse labeling in practice. In this paper, we tackle this challenge by proposing an unsupervised method to learn universal embeddings of time series. Unlike previous works, it is scalable with respect to their length and we demonstrate the quality, transferability and practicability of the learned representations with thorough experiments and comparisons. To this end, we combine an encoder based on causal dilated convolutions with a novel triplet loss employing time-based negative sampling, obtaining general-purpose representations for variable length and multivariate time series.
BibTeX
@inproceedings{NEURIPS2019_53c6de78,
author = {Franceschi, Jean-Yves and Dieuleveut, Aymeric and Jaggi, Martin},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Unsupervised Scalable Representation Learning for Multivariate Time Series},
url = {https://proceedings.neurips.cc/paper_files/paper/2019/file/53c6de78244e9f528eb3e1cda69699bb-Paper.pdf},
volume = {32},
year = {2019}
}