  Graph Neural Networks (GNN) have recently gained popularity in the
forecasting domain due to their ability to model complex spatial and temporal
patterns in tasks such as traffic forecasting and region-based demand
forecasting. Most of these methods require a predefined graph as input, whereas
in real-life multivariate time series problems, a well-predefined dependency
graph rarely exists. This requirement makes it harder for GNNs to be utilised
widely for multivariate forecasting problems in other domains such as retail or
energy. In this paper, we propose a hybrid approach combining neural networks
and statistical structure learning models to self-learn the dependencies and
construct a dynamically changing dependency graph from multivariate data aiming
to enable the use of GNNs for multivariate forecasting even when a well-defined
graph does not exist. The statistical structure modeling in conjunction with
neural networks provides a well-principled and efficient approach by bringing
in causal semantics to determine dependencies among the series. Finally, we
demonstrate significantly improved performance using our proposed approach on
real-world benchmark datasets without a pre-defined dependency graph.
