  Deep learning (DL) in general and Recurrent neural networks (RNNs) in
particular have seen high success levels in sequence based applications. This
paper pertains to RNNs for time series modelling and forecasting. We propose a
novel RNN architecture capturing (stochastic) seasonal correlations
intelligently while capable of accurate multi-step forecasting. It is motivated
from the well-known encoder-decoder (ED) architecture and multiplicative
seasonal auto-regressive model. It incorporates multi-step (multi-target)
learning even in the presence (or absence) of exogenous inputs. It can be
employed on single or multiple sequence data. For the multiple sequence case,
we also propose a novel greedy recursive procedure to build (one or more)
predictive models across sequences when per-sequence data is less. We
demonstrate via extensive experiments the utility of our proposed architecture
both in single sequence and multiple sequence scenarios.
