A two-stage pole-zero predictor is proposed which is capable of predicting minimum phase auto regressive moving average (ARMA) processes accurately with a reduced number of parameters. This is achieved by cascading the classical predictor (1st stage), and the pole-zero recursive-like structure (2nd stage). The constraint on the order of the first stage is relaxed since the unknown process is actually represented by the parameters of the second stage. Computer simulations for both low- and high-order ARMA processes are given to demonstrate the excellent performance of the proposed technique.
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