In this thesis, the foundations for the development of a behavioral approach to linear approximate modeling, are established. A particular data set, consisting of stable, discrete-time, purely exponential time series and a specific class of dynamical models are considered. A misfit function, between the data measurements and a system, belonging to this model class, is defined and the problem of characterizing all members of our model class, for which the value of the misfit function remains below a prespecified error level, is addressed. The concept of the block Hankel matrix, constructed from the data measurements, is then introduced, and it is shown that the optimal Hankel-norm approximation theory provides the main tool for a partial solution of the above problem.