  Missing data scenarios are very common in ML applications in general and
time-series/sequence applications are no exceptions. This paper pertains to a
novel Recurrent Neural Network (RNN) based solution for sequence prediction
under missing data. Our method is distinct from all existing approaches. It
tries to encode the missingness patterns in the data directly without trying to
impute data either before or during model building. Our encoding is lossless
and achieves compression. It can be employed for both sequence classification
and forecasting. We focus on forecasting here in a general context of
multi-step prediction in presence of possible exogenous inputs. In particular,
we propose novel variants of Encoder-Decoder (Seq2Seq) RNNs for this. The
encoder here adopts the above mentioned pattern encoding, while at the decoder
which has a different structure, multiple variants are feasible. We demonstrate
the utility of our proposed architecture via multiple experiments on both
single and multiple sequence (real) data-sets. We consider both scenarios where
(i)data is naturally missing and (ii)data is synthetically masked.
