Stop&Hop: Early Classification of Irregular Time Series

TL;DR

Stop&Hop introduces a novel early classification method for irregular time series, leveraging continuous-time models and reinforcement learning to make timely, accurate predictions in real-world scenarios like healthcare.

Contribution

It develops a new framework combining continuous-time recurrent networks with an irregularity-aware halting policy for early classification of irregular time series.

Findings

Outperforms state-of-the-art methods in early prediction accuracy

Makes earlier predictions while maintaining high accuracy

Demonstrates effectiveness on synthetic and real-world datasets

Abstract

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.