Utilizing Expert Features for Contrastive Learning of Time-Series Representations

Manuel Nonnenmacher · Lukas Oldenburg · Ingo Steinwart · David Reeb

Ballroom 1 & 2
[ Abstract ] [ Livestream: Visit Deep Learning ]
Thu 21 Jul 12:50 p.m. — 12:55 p.m. PDT
[ Slides [ Paper PDF

We present an approach that incorporates expert knowledge for time-series representation learning. Our method employs expert features to replace the commonly used data transformations in previous contrastive learning approaches. We do this since time-series data frequently stems from the industrial or medical field where expert features are often available from domain experts, while transformations are generally elusive for time-series data. We start by proposing two properties that useful time-series representations should fulfill and show that current representation learning approaches do not ensure these properties. We therefore devise ExpCLR, a novel contrastive learning approach built on an objective that utilizes expert features to encourage both properties for the learned representation. Finally, we demonstrate on three real-world time-series datasets that ExpCLR surpasses several state-of-the-art methods for both unsupervised and semi-supervised representation learning.

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