  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.
