As Easy as APC
Comparison of time series models as a function of missing data (x axis), and class imbalance (three plots). We see that baseline models (GRU & GRU-D with class-reweighting) perform significantly worse than models pre-trained with APC (auto-regressive predictive coding). These results suggest APC as an effective self-supervised learning method for handling missing data and class imbalance simultaneously.
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.
Fiorella Wever, T. Anderson Keller, Layra Symul, Victor Garcia Satorras
Accepted at: Self-Supervised Learning workshop at NeurIPS 2021 (Poster)
ArXiv Paper: https://arxiv.org/abs/2106.15577
