Bilateral prediction of stationary Gaussian processes

Let y be a multivariate stationary Gaussian process whose values cannot be measured on a certain finite time interval. The aim of this paper is to present a new approach to optimal least-squares estimation of the missing values of y. Relying and extending on some ideas of stochastic realization theory we derive, in the rational case, the first explicit solution to this classical problem of the prediction theory of stationary processes.