Corpus ID: 59413908

Unsupervised Scalable Representation Learning for Multivariate Time Series

@inproceedings{Franceschi2019UnsupervisedSR,
  title={Unsupervised Scalable Representation Learning for Multivariate Time Series},
  author={Jean-Yves Franceschi and Aymeric Dieuleveut and M. Jaggi},
  booktitle={NeurIPS},
  year={2019}
}

Jean-Yves Franceschi, Aymeric Dieuleveut, M. Jaggi

Published in NeurIPS 2019

Computer Science, Mathematics

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… CONTINUE READING

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