Utilizing Expert Features for Contrastive Learning of Time-Series Representations

Manuel Nonnenmacher · Lukas Oldenburg · Ingo Steinwart · David Reeb

Hall E #433

Keywords: [ DL: Self-Supervised Learning ] [ APP: Time Series ] [ DL: Sequential Models, Time series ] [ DL: Other Representation Learning ]


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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