Abstract
In this study an adaptive algorithm for multivariate (MV) ARMA model order identification and parameter estimation is presented based on the multi-model partitioning theory (MMPT). The method proposed is based on the reformulation of the problem in the standard state space form and on implementing a bank of Kalman filters, each fitting a different order model. The first step will be to select the order of the MV ARMA model using the MPPT, for general (not necessarily Gaussian) data pdf's. The assumption made is that the true model order is \theta (\lambda, \lambda) where \lambda = max (p, q), p is the order of the AR component and q the order of the MA component. The second step will be to estimate the AR and MA coefficients and the actual values of p and q.
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