  We address the problem of prediction of multivariate data process using an
underlying graph model. We develop a method that learns a sparse partial
correlation graph in a tuning-free and computationally efficient manner.
Specifically, the graph structure is learned recursively without the need for
cross-validation or parameter tuning by building upon a hyperparameter-free
framework. Our approach does not require the graph to be undirected and also
accommodates varying noise levels across different nodes.Experiments using
real-world datasets show that the proposed method offers significant
performance gains in prediction, in comparison with the graphs frequently
associated with these datasets.
