A method for designing a finite automaton whose output exhibits a given rational power spectral density R(z) belonging to a particular class, is presented. If the automaton input is composed of independent and identically distributed symbols, its state process is a homogeneous Markov chain whose transition probability matrix pi may by obtained from the input probability mass function and the state transition function. Since the poles of R(z) only depend on pi whereas its zeros depend on the matrix A specifying the output function, a matrix pi with the desired eigenvalues (and perhaps additional ones) is first derived and, the matrix A is determined so as to ensure the realization of the desired zeros (as well as the cancellation of the additional poles possibly introduced in the first step). The method exploits the properties of circulant matrices; in particular, a sufficient condition is provided under which a circular matrix with given eigenvalues (ordered in a Hermitian sequence) is stochastic.>

A variant to a recently proposed autoregressive moving average (ARMA) spectrum estimation technique for time series with gapped data is suggested. It is based on the partial fraction expansion of the power spectrum and exhibits some computational and operational advantages.

Graphic representations may be considered as a tool for deriving a mathematical model from a qualitative description. Forrester's schematics and compartmental graphs are examined from this point of view. Their similarities and differences are then discussed. Two examples taken from the system dynamics literature help clarify the characteristics of both representations.