  Time series forecasting plays a key role in a variety of domains. In a lot of
real-world scenarios, there exist multiple forecasting entities (e.g. power
station in the solar system, stations in the traffic system). A straightforward
forecasting solution is to mine the temporal dependency for each individual
entity through 1d-CNN, RNN, transformer, etc. This approach overlooks the
relations between these entities and, in consequence, loses the opportunity to
improve performance using spatial-temporal relation. However, in many
real-world scenarios, beside explicit relation, there could exist crucial yet
implicit relation between entities. How to discover the useful implicit
relation between entities and effectively utilize the relations for each entity
under various circumstances is crucial. In order to mine the implicit relation
between entities as much as possible and dynamically utilize the relation to
improve the forecasting performance, we propose an attentional multi-graph
neural network with automatic graph learning (A2GNN) in this work.
Particularly, a Gumbel-softmax based auto graph learner is designed to
automatically capture the implicit relation among forecasting entities. We
further propose an attentional relation learner that enables every entity to
dynamically pay attention to its preferred relations. Extensive experiments are
conducted on five real-world datasets from three different domains. The results
demonstrate the effectiveness of A2GNN beyond several state-of-the-art methods.
