Results 231 to 240 of about 7,605 (274)

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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On the closed form of the covariance matrix and its inverse of the causal ARMA process

Journal of Time Series Analysis, 2004
Derivation of the theoretical autocovariance function of a causal autoregressive moving-average process of order (p, q), ARMA(p, q), when q ≥ 1 is considered.
Haddad J.N.
exaly   +2 more sources

On improvement of prediction in arma processes

Series Statistics, 1981
Necessary and sufficient conditions are derived in the paper that enable to decide whether an additional multivariate process will improve the prediction in a given multivariate discrete stationary process. The both processes are assumed to form together a process ARMAm n Further it was investigated wnen one can asser t that the both processes are ...
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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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On shifted multiple arma processes

Series Statistics, 1981
Let {X t} be a p-dimensional ARMA (m n) process. Write where have q and r components, respecctivelyq+r=p). Put . It is proved that {Y t} is an ARMA (m -1. n + 1) process and a procedure for evaluation of its matrices of coefficients is given. If {X t} is an AR (m) process, then {Y t is an AR (m -f1) process; if {X,tis an MA(n-) process, then {Y t} is ...
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INFINITE VARIANCE STABLE ARMA PROCESSES

Journal of Time Series Analysis, 1994
Abstract. The asymptotic dependence structure of autoregressive moving‐average processes with stable innovations is analyzed. The analysis is carried out by means of a measure of dependence which extends the covariance function and is applicable to stochastic processes with infinite variance.
Kokoszka, Piotr S., Taqqu, Murad 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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