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.