  An emerging way of tackling the dimensionality issues arising in the modeling
of a multivariate process is to assume that the inherent data structure can be
captured by a graph. Nevertheless, though state-of-the-art graph-based methods
have been successful for many learning tasks, they do not consider
time-evolving signals and thus are not suitable for prediction. Based on the
recently introduced joint stationarity framework for time-vertex processes,
this letter considers multivariate models that exploit the graph topology so as
to facilitate the prediction. The resulting method yields similar accuracy to
the joint (time-graph) mean-squared error estimator but at lower complexity,
and outperforms purely time-based methods.
