  Continuous-time models such as Neural ODEs and Neural Flows have shown
promising results in analyzing irregularly sampled time series frequently
encountered in electronic health records. Based on these models, time series
are typically processed with a hybrid of an initial value problem (IVP) solver
and a recurrent neural network within the variational autoencoder architecture.
Sequentially solving IVPs makes such models computationally less efficient. In
this paper, we propose to model time series purely with continuous processes
whose state evolution can be approximated directly by IVPs. This eliminates the
need for recurrent computation and enables multiple states to evolve in
parallel. We further fuse the encoder and decoder with one IVP solver utilizing
its invertibility, which leads to fewer parameters and faster convergence.
Experiments on three real-world datasets show that the proposed method can
systematically outperform its predecessors, achieve state-of-the-art results,
and have significant advantages in terms of data efficiency.
