  We propose an end-to-end architecture for multivariate time-series prediction
that integrates a spatial-temporal graph neural network with a matrix filtering
module. This module generates filtered (inverse) correlation graphs from
multivariate time series before inputting them into a GNN. In contrast with
existing sparsification methods adopted in graph neural network, our model
explicitly leverage time-series filtering to overcome the low signal-to-noise
ratio typical of complex systems data. We present a set of experiments, where
we predict future sales from a synthetic time-series sales dataset. The
proposed spatial-temporal graph neural network displays superior performances
with respect to baseline approaches, with no graphical information, and with
fully connected, disconnected graphs and unfiltered graphs.
