Abstract
The realization of Markovian models for nonstationary processes generated by time invariant linear systems is considered. A model is obtained by constructing an orthogonal finite-step predictor for the process. Optimal model approximation by order reduction is naturally defined in this framework. The construction and reduction of Markovian models from multiple data records and the numerical considerations involved are illustrated by examples.
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