Results 221 to 230 of about 4,998 (266)

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.
Sabiti, Kiseta
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ARMA Processes

2022
AbstractChapter 5 enters into stochastic time series analysis with the description of moving average, autoregressive and autoregressive moving average processes. Seasonal time series analysis is introduced with examples applied to measures temperature and the hydrological cycle.
Marco Bittelli, Roberto Olmi, Rodolfo Rosa   +2 more
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Computation of cumulants of ARMA processes

International Conference on Acoustics, Speech, and Signal Processing, 2003
Using the observable state-space realization corresponding to a given multi-input-multi-output autoregressive moving average (ARMA) model, the authors derive closed-form and lag-recursive expressions for the cumulants of the output process. Their approach involves the computation of cumulants of vector processes, which they define compactly in terms of
Ananthram Swami, Jerry M. Mendel
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ARMA processes: order estimation

ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing, 2003
The authors study, for an ARMA (autoregressive moving-average) (p/sub 0/, q/sub 0/) process, the joint determination from a finite data sample of its structural parameters p/sub 0/ and q/sub 0/, its AR and MA components, and its innovation power sigma /sup 2/.
M. Isabel Ribeiro, José M. F. Moura
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A metric for ARMA processes

IEEE Transactions on Signal Processing, 2000
Summary: Autoregressive-moving-average (ARMA) models seek to express a system function of a discretely sampled process as a rational function in the \(z\)-domain. Treating an ARMA model as a complex rational function, we discuss a metric defined on the set of complex rational functions.
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On Estimating the Order of an ARMA Process

IFAC Proceedings Volumes, 1987
In this brief paper the author describes an order determination test for ARMA processes using matrix perturbation theory. The test requires the selection of the significance level on subjective grounds. The test is numerically simple, and the results are reported as good.
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The identification of ARMA processes

Journal of Applied Probability, 1986
This paper presents a review of recent results for the identification of ARMA processes according to the principles introduced by Akaike, i.e. assuming that the true orders exist and proposing criteria such as AIC and BIC. The development both of these methods and of consistency theory has been led by E. J. Hannan.
An Hong-Zhi, Chen Zhao-Guo
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Simple correlated arma processes

Series Statistics, 1984
Let \(\{x_ t\}\) and \(\{y_ t\}\) be ARMA (autoregressive-moving average) processes, defined in terms of operators acting respectively on processes \(a_ t\) and \(b_ t\) such that \((a_ t,b_ t)\) is bivariate normal with means 0, variances \(\sigma^ 2_ a\) and \(\sigma^ 2_ b\), and correlation \(\rho\). Then the bivariate process \(\{(x_ t,y_ t)\}\) is
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Modelling and Forecasting with ARMA Processes

1996
The determination of an appropriate ARMA(p, q) model to represent an observed stationary time series involves a number of interrelated problems. These include the choice of p and q (order selection) and estimation of the mean, the coefficients {ϕ i , i = 1, …, p}, {θ i , i = 1, …, q}, and the white noise variance σ2.
Peter J. Brockwell, Richard A. Davis
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Gaussian maximum likelihood estimation for ARMA models II: Spatial processes

Bernoulli, 2006
This paper examines the Gaussian maximum likelihood estimator (GMLE) in the context of a general form of spatial autoregressive and moving average (ARMA) processes with finite second moment.
Qiwei Yao, Peter J Brockwell
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