Skip to Main content Skip to Navigation
Conference papers

Unsupervised Scalable Representation Learning for Multivariate Time Series

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.
Complete list of metadata

Cited literature [36 references]  Display  Hide  Download
Contributor : Jean-Yves Franceschi Connect in order to contact the contributor
Submitted on : Wednesday, December 18, 2019 - 3:53:55 PM
Last modification on : Wednesday, March 16, 2022 - 3:44:24 AM


Distributed under a Creative Commons Attribution 4.0 International License


  • HAL Id : hal-01998101, version 4
  • ARXIV : 1901.10738



Jean-Yves Franceschi, Aymeric Dieuleveut, Martin Jaggi. Unsupervised Scalable Representation Learning for Multivariate Time Series. Thirty-third Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, Dec 2019, Vancouver, Canada. pp.4650--4661. ⟨hal-01998101v4⟩



Record views


Files downloads