Graph-Guided Network for Irregularly Sampled Multivariate Time Series

In many domains, including healthcare, biology, and climate science, time series are irregularly sampled with variable time between successive observations and different subsets of variables (sensors) are observed at different time points, even after alignment to start events. These data create multiple challenges for prevailing models that assume fully observed and fixed-length feature representations. To address these challenges, it is essential to understand the relationships between sensors and how they evolve over time. It considers both inter-sensor relationships shared across samples and those unique to each sample that can vary with time, and it adaptively estimates misaligned observations based on nearby observations. Multivariate time series are prevalent in a variety of domains including healthcare, space science, cybersecurity, biology, and finance (Ravuri et al., 2021; Sousa et al., 2020; Sezer et al., 2020; Fawaz et al., 2019; Abanda et al., 2019; Tang et al., 2018). Practical issues often exist in collecting sensor measurements that lead to various types of irregularities caused by missing observations, such as cost saving, sensor failures, external forces in physical scenarios, medical interventions, to name a few (Choi et al., 2020). While temporal machine learning models usually assume fully observable and fixed-size input data, irregularly sampled time series raise considerable challenges. For example, the observations of multiple sensors are not well-aligned; the time intervals among adjacent observations are different across sensors; and different samples have different numbers of observations for different subsets of sensors recorded at different time points.