  Forecasting of multivariate time-series is an important problem that has
applications in traffic management, cellular network configuration, and
quantitative finance. A special case of the problem arises when there is a
graph available that captures the relationships between the time-series. In
this paper we propose a novel learning architecture that achieves performance
competitive with or better than the best existing algorithms, without requiring
knowledge of the graph. The key element of our proposed architecture is the
learnable fully connected hard graph gating mechanism that enables the use of
the state-of-the-art and highly computationally efficient fully connected
time-series forecasting architecture in traffic forecasting applications.
Experimental results for two public traffic network datasets illustrate the
value of our approach, and ablation studies confirm the importance of each
element of the architecture. The code is available here:
https://github.com/boreshkinai/fc-gaga.
