  Irregularly-sampled time series (ITS) are native to high-impact domains like
healthcare, where measurements are collected over time at uneven intervals.
However, for many classification problems, only small portions of long time
series are often relevant to the class label. In this case, existing ITS models
often fail to classify long series since they rely on careful imputation, which
easily over- or under-samples the relevant regions. Using this insight, we then
propose CAT, a model that classifies multivariate ITS by explicitly seeking
highly-relevant portions of an input series' timeline. CAT achieves this by
integrating three components: (1) A Moment Network learns to seek relevant
moments in an ITS's continuous timeline using reinforcement learning. (2) A
Receptor Network models the temporal dynamics of both observations and their
timing localized around predicted moments. (3) A recurrent Transition Model
models the sequence of transitions between these moments, cultivating a
representation with which the series is classified. Using synthetic and real
data, we find that CAT outperforms ten state-of-the-art methods by finding
short signals in long irregular time series.
