  Time series are often complex and rich in information but sparsely labeled
and therefore challenging to model. In this paper, we propose a self-supervised
framework for learning generalizable representations for non-stationary time
series. Our approach, called Temporal Neighborhood Coding (TNC), takes
advantage of the local smoothness of a signal's generative process to define
neighborhoods in time with stationary properties. Using a debiased contrastive
objective, our framework learns time series representations by ensuring that in
the encoding space, the distribution of signals from within a neighborhood is
distinguishable from the distribution of non-neighboring signals. Our
motivation stems from the medical field, where the ability to model the dynamic
nature of time series data is especially valuable for identifying, tracking,
and predicting the underlying patients' latent states in settings where
labeling data is practically impossible. We compare our method to recently
developed unsupervised representation learning approaches and demonstrate
superior performance on clustering and classification tasks for multiple
datasets.
