Combining pattern recognition techniques with Akaike's information criteria for identifying ARMA models

ARMA models are identified by combining pattern recognition techniques with Akaike's (1974, 1979) information criteria. First, pattern vectors of ARMA models are obtained by the extended sample autocorrelation functions method proposed by Tsay and Tiao (1984). Second, decision functions of various training samples are specified by the perceptron algorithm used in learning machines. Third, Akaike's AIC and BIC criteria are introduced. The practical utility of the proposed approach is illustrated by both simulated and practical data.<
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