  Various contrastive learning approaches have been proposed in recent years
and achieve significant empirical success. While effective and prevalent,
contrastive learning has been less explored for time series data. A key
component of contrastive learning is to select appropriate augmentations
imposing some priors to construct feasible positive samples, such that an
encoder can be trained to learn robust and discriminative representations.
Unlike image and language domains where ``desired'' augmented samples can be
generated with the rule of thumb guided by prefabricated human priors, the
ad-hoc manual selection of time series augmentations is hindered by their
diverse and human-unrecognizable temporal structures. How to find the desired
augmentations of time series data that are meaningful for given contrastive
learning tasks and datasets remains an open question. In this work, we address
the problem by encouraging both high \textit{fidelity} and \textit{variety}
based upon information theory. A theoretical analysis leads to the criteria for
selecting feasible data augmentations. On top of that, we propose a new
contrastive learning approach with information-aware augmentations, InfoTS,
that adaptively selects optimal augmentations for time series representation
learning. Experiments on various datasets show highly competitive performance
with up to 12.0\% reduction in MSE on forecasting tasks and up to 3.7\%
relative improvement in accuracy on classification tasks over the leading
baselines.
