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A procedure is presented for generating an autoregressive moving average (ARMA) spectral model of a stationary time series based upon a finite set of time series' observations. The ARMA model's autoregressive coefficients are estimated by minimizing a quadratic function of a set of basic error terms. In examples treated to date, this method has demonstrated an exceptional ability in resolving closely spaced narrow band signals in a low signal-to-noise environment where other procedures such as the maximum entropy method often fail. Its effectiveness on other classes of time series also shows promise and a more general evaluation is presently being conducted. With this in mind, the new ARMA procedure promises to be an important spectral estimation tool.
Geoscience and Remote Sensing, IEEE Transactions on (Volume:GE-19 , Issue: 1 )
Date of Publication: Jan. 1981