  Sparse and irregularly sampled multivariate time series are common in
clinical, climate, financial and many other domains. Most recent approaches
focus on classification, regression or forecasting tasks on such data. In
forecasting, it is necessary to not only forecast the right value but also to
forecast when that value will occur in the irregular time series. In this work,
we present an approach to forecast not only the values but also the time at
which they are expected to occur.
