Adaptive ARMA spectral estimation
JAMES A. CADZOW, Koji Ogino · 2005
A novel adaptive method for efficiently obtaining an ARMA model spectral estimate of a wide-sense stationary time series is presented. It is adaptive in the sense that as a new element of the time series is observed, the coefficients of a (p,p)th order ARMA model may be algorithmically updated. This algorithm's computational complexity (i.e., the number of multiplications and additions required) is of the orderp \log(p)for a particular version of the method. Moreover, the spectral estimation performance of this new method is found typically to be far superior to such contemporary approaches as the Box-Jenkins, maximum entropy, and, Widrow's LMS methods. This performance in conjunction with its computational efficiency mark this algorithm as being a primary spectral estimation tool.