  Established recurrent neural networks are well-suited to solve a wide variety
of prediction tasks involving discrete sequences. However, they do not perform
as well in the task of dynamical system identification, when dealing with
observations from continuous variables that are unevenly sampled in time, for
example due to missing observations. We show how such neural sequence models
can be adapted to deal with variable step sizes in a natural way. In
particular, we introduce a time-aware and stationary extension of existing
models (including the Gated Recurrent Unit) that allows them to deal with
unevenly sampled system observations by adapting to the observation times,
while facilitating higher-order temporal behavior. We discuss the properties
and demonstrate the validity of the proposed approach, based on samples from
two industrial input/output processes.
