  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.
