Stochastic state-space models from empirical data

A technique is described for developing state-space models from vector time series. The technique is based on canonical variates analysis: a form of least-squares multi-step linear prediction. Unlike Gaussian maximum likelihood and one-step linear prediction techniques for state-space modeling, state-space models are generated by solving a finite number of linear equations. The approach is suited to off-line modeling and fragmented data sets. The technique has been used for spectrum estimation, reduced-order modeling, and Kalman filtering.