1975IEEE Transactions on Automatic ControlRequires access

Identification of autoregressive moving-average parameters of time series

Daniel Graupe, D. J. KRAUSE, Jason H. Moore

Open publisher page 97 citations

Abstract

A procedure for sequentially estimating the parameters and orders of mixed autoregressive moving-average signal models from time-series data is presented. Identification is performed by first identifying a purely autoregressive signal model. The parameters and orders of the mixed autoregressive moving-average process are then given from the solution of simple algebraic equations involving the purely autoregressive model parameters.

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What this paper is about

A procedure for sequentially estimating the parameters and orders of mixed autoregressive moving-average signal models from time-series data is presented. Identification is performed by first identifying a purely autoregressive signal model. The parameters and orders of the mixed autoregressive moving-average process are then given from the solution of simple algebraic equations involving the purely autoregressive model parameters.

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OpenAlex reports 97 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

A procedure for sequentially estimating the parameters and orders of mixed autoregressive moving-average signal models from time-series data is presented. Identification is performed by first identifying a purely autoregressive signal model. The parameters and orders of the mixed autoregressive moving-average process are then given from the solution of simple algebraic equations involving the purely autoregressive model parameters.

Key concepts: Autoregressive model, STAR model, Nonlinear autoregressive exogenous model, SETAR, Series (stratigraphy), Autoregressive–moving-average model, Autoregressive integrated moving average, Time series

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