ELLIS header
University of Stuttgart Logo
Max Planck Institute for Intelligent Systems Logo

Utilizing Expert Features for Contrastive Learning of Time-Series Representations

Manuel Nonnenmacher, Lukas Oldenburg, Ingo Steinwart, David Reeb

Proc. of the 39th International Conference on Machine Learning (ICML), pp. 1–21, 2022.


Abstract

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.

Links


BibTeX

@inproceedings{nonnenmacher22_icml, title = {Utilizing Expert Features for Contrastive Learning of Time-Series Representations}, author = {Nonnenmacher, Manuel and Oldenburg, Lukas and Steinwart, Ingo and Reeb, David}, year = {2022}, booktitle = {Proc. of the 39th International Conference on Machine Learning (ICML)}, pages = {1--21} }