  We present a dynamic prediction framework for binary sequences that is based
on a Bernoulli generalization of the auto-regressive process. Our approach
lends itself easily to variants of the standard link prediction problem for a
sequence of time dependent networks. Focusing on this dynamic network link
prediction/recommendation task, we propose a novel problem that exploits
additional information via a much larger sequence of auxiliary networks and has
important real-world relevance. To allow discovery of links that do not exist
in the available data, our model estimation framework introduces a
regularization term that presents a trade-off between the conventional link
prediction and this discovery task. In contrast to existing work our stochastic
gradient based estimation approach is highly efficient and can scale to
networks with millions of nodes. We show extensive empirical results on both
actual product-usage based time dependent networks and also present results on
a Reddit based data set of time dependent sentiment sequences.
