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

TL;DR

This paper demonstrates that Autoregressive Predictive Coding (APC), a self-supervised learning method, can effectively address both missing data and class imbalance in time series, improving classification performance especially in challenging real-world datasets.

Contribution

The work introduces a unified self-supervised approach using APC to handle missing data and class imbalance simultaneously without strong assumptions, outperforming existing methods.

Findings

APC significantly improves performance on synthetic data with missingness and imbalance.

APC achieves state-of-the-art AUPRC on Physionet benchmark.

Consistent performance gains observed on real-world medical datasets.

Abstract

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.