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

Authors:
Liang Wang;G.A. Libert
Affiliations:
Dept. of Electr. Eng., Texas A&M Univ., College Station, TX;-
Venue:
IEEE Transactions on Signal Processing
Year:
1994

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Low-level interpretability and high-level interpretability: a unified view of data-driven interpretable fuzzy system modelling

Fuzzy Sets and Systems

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Abstract

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