Results 241 to 250 of about 7,605 (274)

Generation of the autocorrelation sequence of an ARMA process

IEEE Transactions on Acoustics, Speech, and Signal Processing, 1985
A new technique is described for generating the autocorrelation sequence of an autoregressive-moving average process. Unlike some other approaches, the method does not require a matrix inversion.
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Variability of ARMA Processes

1996
In this chapter, the numerical and pictorial interpretation of the dependence of the standard deviation of the forecast error for the different types and orders of univariate autoregressive-moving average (ARMA) processes on the lead time and on the autocorrelations (in the domains of the permissible autocorrelations) are given.
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Feedback Linear Estimation of ARMA Processes

IFAC Proceedings Volumes, 1985
Abstract A four-step algorithm of FLE (Feedback Linear Estimation) based on the feedback control principle for ARMA processes is presented in this paper. The proposed algorithm uses three linear least square estimators and a linear filter. Linear estimations are utilized as a tool throughout the algorithm. The physical meaning of FLE is discussed and
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A fast estimation method for ARMA processes

Automatica, 1996
A computationally effective two-stage least squares procedure for parameter estimation of Gaussian ARMA processes is presented and discussed. The asymptotic properties of the estimates are analyzed and their equivalence to the exact maximum likelihood method is shown. A comparative simulation study is included.
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Innovation algorithm in ARMA process

Korean Journal of Computational & Applied Mathematics, 1998
Summary: Most of the works in Time Series Analysis are based on the Auto Regressive Integrated Moving Average (ARIMA) Box and Jenkins models. If the data exhibits no apparent deviation from stationarity and if it has rapidly decreasing autocorrelation function then a suitable ARMA\((p,q)\) model is fit to the given data. Selection of the orders of \(p\)
Sreenivasan, M., Sumathi, K.
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The Estimation of ARMA Processes

1984
A description is given of a method for estimating an ARMA process, y(t) , from observations for t=1, 2, ...T, and following this a discussion is given of the theory necessary for the validation of the method. The first stage of the method involves the fitting of an autoregression, of order hT determined by a criterion such as AIC. The asymptotic theory
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ARMA Systems and ARMA Processes

2022
Manfred Deistler, Wolfgang Scherrer
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Univariate ARMA Processes

2017
If q = 0, then X t is also called an autoregressive process of order p, or AR(p) process. If p = 0, then X t is also called a moving average process of order p, or MA(q) process.
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On transformations of multivariate ARMA processes

Kybernetika, 1988
Summary: Transformations of multivariate ARMA processes are investigated such that they preserve the ARMA structure. A theorem is given that characterizes a multivariate ARMA process using a property of its covariance function. The theorem is applied to the linear transformation of a multivariate ARMA process and to the scalar product of two ARMA ...
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On the Calibration of ARMA Processes for Simulation

1987
Auto-Regressive Moving-Average processes have found increased application in recent years in connection with simulation of stochastic loads on structures. Computational efficiency and limitations on available time for simulation in connection with tests require the number of coefficients in the ARMA process to be small, thereby stressing the need for ...
S. Krenk, J. Clausen
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