  Many time series are generated by a set of entities that interact with one
another over time. This paper introduces a broad, flexible framework to learn
from multiple inter-dependent time series generated by such entities. Our
framework explicitly models the entities and their interactions through time.
It achieves this by building on the capabilities of Recurrent Neural Networks,
while also offering several ways to incorporate domain knowledge/constraints
into the model architecture. The capabilities of our approach are showcased
through an application to weather prediction, which shows gains over strong
baselines.
