  By interpreting the forward dynamics of the latent representation of neural
networks as an ordinary differential equation, Neural Ordinary Differential
Equation (Neural ODE) emerged as an effective framework for modeling a system
dynamics in the continuous time domain. However, real-world systems often
involves external interventions that cause changes in the system dynamics such
as a moving ball coming in contact with another ball, or such as a patient
being administered with particular drug. Neural ODE and a number of its recent
variants, however, are not suitable for modeling such interventions as they do
not properly model the observations and the interventions separately. In this
paper, we propose a novel neural ODE-based approach (IMODE) that properly model
the effect of external interventions by employing two ODE functions to
separately handle the observations and the interventions. Using both synthetic
and real-world time-series datasets involving interventions, our experimental
results consistently demonstrate the superiority of IMODE compared to existing
approaches.
