Abstract
Abstract Three procedures for the classification of multivariate time-dependent data are examined. Two are shown to be Bayes procedures assuming that the time dependence of the data can be described by polynomial regression functions and that the relation among successive residuals is described by a stationary and invertible ARMA process. Relative advantages of these procedures in practical applications and limitations of the model are discussed.
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