As easy as APC: overcoming missing data and class imbalance in time series with self-supervised learning. (arXiv:2106.15577v5 [cs.LG] UPDATED)

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, …

apc arxiv data learning self-supervised learning supervised learning time time series