  High levels of missing data and strong class imbalance are ubiquitous
challenges that are often presented simultaneously in real-world time series
data. Existing methods approach these problems separately, frequently making
significant assumptions about the underlying data generation process in order
to lessen the impact of missing information. In this work, we instead
demonstrate how a general self-supervised training method, namely
Autoregressive Predictive Coding (APC), can be leveraged to overcome both
missing data and class imbalance simultaneously without strong assumptions.
Specifically, on a synthetic dataset, we show that standard baselines are
substantially improved upon through the use of APC, yielding the greatest gains
in the combined setting of high missingness and severe class imbalance. We
further apply APC on two real-world medical time-series datasets, and show that
APC improves the classification performance in all settings, ultimately
achieving state-of-the-art AUPRC results on the Physionet benchmark.
