  The paper deals with the problem of identifying the internal dependencies and
similarities among a large number of random processes. Linear models are
considered to describe the relations among the time series and the energy
associated to the corresponding modeling error is the criterion adopted to
quantify their similarities. Such an approach is interpreted in terms of graph
theory suggesting a natural way to group processes together when one provides
the best model to explain the other. Moreover, the clustering technique
introduced in this paper will turn out to be the dynamical generalization of
other multivariate procedures described in literature.
