  The modeling of time-varying graph signals as stationary time-vertex
stochastic processes permits the inference of missing signal values by
efficiently employing the correlation patterns of the process across different
graph nodes and time instants. In this study, we propose an algorithm for
computing graph autoregressive moving average (graph ARMA) processes based on
learning the joint time-vertex power spectral density of the process from its
incomplete realizations for the task of signal interpolation. Our solution
relies on first roughly estimating the joint spectrum of the process from
partially observed realizations and then refining this estimate by projecting
it onto the spectrum manifold of the graph ARMA process through convex
relaxations. The initially missing signal values are then estimated based on
the learnt model. Experimental results show that the proposed approach achieves
high accuracy in time-vertex signal estimation problems.
