Results 171 to 180 of about 4,545 (215)

Linear estimation of ARMA processes

Fourth Annual ASSP Workshop on Spectrum Estimation and Modeling, 2003
The authors propose an identification algorithm for autoregressive moving average (ARMA) processes. Given a finite length sample drawn from an ARMA (p/sub 0/, q/sub 0/) model, the technique provides the estimated values of the orders p/sub 0/ and p/sub 0/, as well as the AR and MA coefficients. They are obtained from the reflection coefficient sequence
J.M.F. Moura, M.I. Ribeiro
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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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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 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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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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Exit times for ARMA processes

Advances in Applied Probability, 2018
Abstract We study the asymptotic behaviour of the expected exit time from an interval for the ARMA process, when the noise level approaches 0.
Koski, Timo, Jung, Brita, Högnäs, Göran   +2 more
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Continuous-time ARMA processes

2001
Continuous-time autoregressive (CAR) processes have been of interest to physicists and engineers for many years (see e.g., Fowler, 1936 ). Early papers dealing with the properties and statistical analysis of such processes, and of the more general continuous-time autoregressive moving average (CARMA) processes, include those of Doob (1944 ...
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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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Multivariate Periodic ARMA(1,1) Processes

Water Resources Research, 1988
The main objective of this paper is to investigate the properties of multivariate periodic autoregressive moving average (ARMA)(l, 1) processes. Such properties include the covariance structure, the parameter space, and estimation. The parameter space of such periodic processes is derived by aggregation (for instance, monthly models will aggregate to ...
BARTOLINI, PAOLO, J. D. Salas, J. Obeysekera   +2 more
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ARMA(p,q) Processes

2018
A long-standing dream of economists was to build a massive model of the economy. One with hundreds of supply and demand equations. A supply and demand system for each input, intermediate good, and final product. One would only need to estimate the relevant elasticities and a few simple parameters to construct an economic crystal ball.
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