Results 231 to 240 of about 4,998 (266)

Model Identification of Integrated ARMA Processes

Multivariate Behavioral Research, 2008
This article evaluates the Smallest Canonical Correlation Method (SCAN) and the Extended Sample Autocorrelation Function (ESACF), automated methods for the Autoregressive Integrated Moving-Average (ARIMA) model selection commonly available in current versions of SAS for Windows, as identification tools for integrated processes.
Tetiana, Stadnytska, Simone, Braun, Joachim, Werner   +2 more
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The quality of models for ARMA processes

IEEE Transactions on Signal Processing, 1998
The model error (ME) is an objective measure for assessing the quality of different models of a given ARMA process. The expression for ME can be evaluated easily in the time domain. This quality measure for known and given processes is necessary for an objective comparison of the performance of estimation algorithms and of order selection criteria.
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A Note on Non‐Negative Arma Processes

Journal of Time Series Analysis, 2006
Abstract.  Recently, there has been much research on developing models suitable for analysing the volatility of a discrete‐time process. Since the volatility process, like many others, is necessarily non‐negative, there is a need to construct models for stationary processes which are non‐negative with probability one.
Tsai, Henghsiu, Chan, K. S.
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A Class of Non‐Embeddable ARMA Processes

Journal of Time Series Analysis, 1999
We show that a stationary ARMA(p, q) process {Xn = 0, 1, 2, ...} whose moving‐average polynomial has a root on the unit circle cannot be embedded in any continuous‐time autoregressive moving‐average (ARMA) process {Y}(t), t≥ 0}, i.e. we show that it is impossible to find a continuous‐time ARMA process {Y}(t)} whose autocovariance function at integer ...
Brockwell, Anthony E., Brockwell, Peter J.   +1 more
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Stationary ARMA Processes

1987
In this chapter we introduce an extremely important class of time series {X t , t = 0, ± 1, ± 2,...} defined in terms of linear difference equations with constant coefficients. The imposition of this additional structure defines a parametric family of stationary processes, the autoregressive moving average or ARMA processes.
Peter J. Brockwell, Richard A. Davis
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On the maximal entropy property for ARMA processes and ARMA approximation

Advances in Applied Probability, 1990
The existence and properties of a general ARMA ( p, q ) process, whose autocovariances, up to lag p , and impulse coefficients, up to lag q, coincide with some given values, are shown.
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Inference and prediction with arma processes

Communications in Statistics - Theory and Methods, 1985
An essential ingredient of any time series analysis is the estimation of the model parameters and the forecasting of future observations. This investigation takes a Bayesian approach to the analysis of time series by making inferences of the model parameters from the posterior distribution and forecasting from the predictive distribution.
Samir Shaarawy, Lyle Broemeling
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On Fisher’s Information Matrix of an ARMA Process

1997
In this paper we study the Fisher information matrix for a stationary ARMA process with the aid of Sylvester’s resultant matrix. Some properties are explained via realizations in state space form of the derivates of the white noise process with respect to the parameters.
Klein, A., Spreij, P.
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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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