  Irregularly sampled time series with missing values are often observed in
multiple real-world applications such as healthcare, climate and astronomy.
They pose a significant challenge to standard deep learn- ing models that
operate only on fully observed and regularly sampled time series. In order to
capture the continuous dynamics of the irreg- ular time series, many models
rely on solving an Ordinary Differential Equation (ODE) in the hidden state.
These ODE-based models tend to perform slow and require large memory due to
sequential operations and a complex ODE solver. As an alternative to complex
ODE-based mod- els, we propose a family of models called Functional Latent
Dynamics (FLD). Instead of solving the ODE, we use simple curves which exist at
all time points to specify the continuous latent state in the model. The
coefficients of these curves are learned only from the observed values in the
time series ignoring the missing values. Through extensive experi- ments, we
demonstrate that FLD achieves better performance compared to the best ODE-based
model while reducing the runtime and memory overhead. Specifically, FLD
requires an order of magnitude less time to infer the forecasts compared to the
best performing forecasting model.
