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arXiv · 2311.11335

Self-Distilled Representation Learning for Time Series

Felix Pieper·Konstantin Ditschuneit·Martin Genzel·Alexandra Lindt·Johannes Otterbach
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Abstract

Self-supervised learning for time-series data holds potential similar to that recently unleashed in Natural Language Processing and Computer Vision. While most existing works in this area focus on contrastive learning, we propose a conceptually simple yet powerful non-contrastive approach, based on the data2vec self-distillation framework. The core of our method is a student-teacher scheme that predicts the latent representation of an input time series from masked views of the same time series. This strategy avoids strong modality-specific assumptions and biases typically introduced by the design of contrastive sample pairs. We demonstrate the competitiveness of our approach for classification and forecasting as downstream tasks, comparing with state-of-the-art self-supervised learning methods on the UCR and UEA archives as well as the ETT and Electricity datasets.

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Felix Pieper, Konstantin Ditschuneit, Martin Genzel, Alexandra Lindt, Johannes Otterbach. 2023-11-19. Self-Distilled Representation Learning for Time Series. https://arxiv.org/abs/2311.11335

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