  Early classification algorithms help users react faster to their machine
learning model's predictions. Early warning systems in hospitals, for example,
let clinicians improve their patients' outcomes by accurately predicting
infections. While early classification systems are advancing rapidly, a major
gap remains: existing systems do not consider irregular time series, which have
uneven and often-long gaps between their observations. Such series are
notoriously pervasive in impactful domains like healthcare. We bridge this gap
and study early classification of irregular time series, a new setting for
early classifiers that opens doors to more real-world problems. Our solution,
Stop&Hop, uses a continuous-time recurrent network to model ongoing irregular
time series in real time, while an irregularity-aware halting policy, trained
with reinforcement learning, predicts when to stop and classify the streaming
series. By taking real-valued step sizes, the halting policy flexibly decides
exactly when to stop ongoing series in real time. This way, Stop&Hop seamlessly
integrates information contained in the timing of observations, a new and vital
source for early classification in this setting, with the time series values to
provide early classifications for irregular time series. Using four synthetic
and three real-world datasets, we demonstrate that Stop&Hop consistently makes
earlier and more-accurate predictions than state-of-the-art alternatives
adapted to this new problem. Our code is publicly available at
https://github.com/thartvigsen/StopAndHop.

Machine Learning