  In many domains, including healthcare, biology, and climate science, time
series are irregularly sampled with varying time intervals between successive
readouts and different subsets of variables (sensors) observed at different
time points. Here, we introduce RAINDROP, a graph neural network that embeds
irregularly sampled and multivariate time series while also learning the
dynamics of sensors purely from observational data. RAINDROP represents every
sample as a separate sensor graph and models time-varying dependencies between
sensors with a novel message passing operator. It estimates the latent sensor
graph structure and leverages the structure together with nearby observations
to predict misaligned readouts. This model can be interpreted as a graph neural
network that sends messages over graphs that are optimized for capturing
time-varying dependencies among sensors. We use RAINDROP to classify time
series and interpret temporal dynamics on three healthcare and human activity
datasets. RAINDROP outperforms state-of-the-art methods by up to 11.4%
(absolute F1-score points), including techniques that deal with irregular
sampling using fixed discretization and set functions. RAINDROP shows
superiority in diverse setups, including challenging leave-sensor-out settings.
