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Poster
Unsupervised Scalable Representation Learning for Multivariate Time Series
Jean-Yves Franceschi · Aymeric Dieuleveut · Martin Jaggi

Thu Dec 12 10:45 AM -- 12:45 PM (PST) @ East Exhibition Hall B + C #114
in Applications -- Time Series Analysis »

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.

Keywords: Time Series Analysis · Applications · Algorithms -> Classification; Algorithms -> Representation Learning; Algorithms -> Unsupervised Learning; Deep Learning · CNN
[ Paper]  [ Poster] 

Author Information

Jean-Yves Franceschi (Sorbonne Université)
Aymeric Dieuleveut (Ecole Polytechnique, IPParis)
Martin Jaggi (EPFL)

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