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High levels of missing data and strong class imbalance are ubiquitouschallenges that are often presented simultaneously in real-world time seriesdata. Existing methods approach these problems separately, frequently makingsignificant assumptions about the underlying data generation process in orderto lessen the impact of missing information. In this work, we insteaddemonstrate how a general self-supervised training method, namelyAutoregressive Predictive Coding (APC), can be leveraged to overcome bothmissing data and class imbalance simultaneously without strong assumptions.Specifically, on a synthetic dataset, we show that standard baselines aresubstantially improved upon through the use of APC, yielding the greatest gainsin the combined setting of high missingness and severe class imbalance. Wefurther apply APC on two real-world medical time-series datasets, and show thatAPC improves the classification performance in all settings, ultimatelyachieving state-of-the-art AUPRC results on the Physionet benchmark.

As easy as APC: overcoming missing data and class imbalance in time series with self-supervised learning