  The essence of multivariate sequential learning is all about how to extract
dependencies in data. These data sets, such as hourly medical records in
intensive care units and multi-frequency phonetic time series, often time
exhibit not only strong serial dependencies in the individual components (the
"marginal" memory) but also non-negligible memories in the cross-sectional
dependencies (the "joint" memory). Because of the multivariate complexity in
the evolution of the joint distribution that underlies the data generating
process, we take a data-driven approach and construct a novel recurrent network
architecture, termed Memory-Gated Recurrent Networks (mGRN), with gates
explicitly regulating two distinct types of memories: the marginal memory and
the joint memory. Through a combination of comprehensive simulation studies and
empirical experiments on a range of public datasets, we show that our proposed
mGRN architecture consistently outperforms state-of-the-art architectures
targeting multivariate time series.
