Results 211 to 220 of about 4,093 (257)
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IEEE Transactions on Pattern Analysis and Machine Intelligence, 1982
A method for efficiently generating a rational model of a wide-sense stationary time series is presented. In this method the autoregressive parameters associated with an ARMA model consisting of q zeros and p poles are optimally chosen with the selection being based on a finite set of time series observations.
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A method for efficiently generating a rational model of a wide-sense stationary time series is presented. In this method the autoregressive parameters associated with an ARMA model consisting of q zeros and p poles are optimally chosen with the selection being based on a finite set of time series observations.
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ON EMBEDDING A DISCRETE‐PARAMETER ARMA MODEL IN A CONTINUOUS‐PARAMETER ARMA MODEL
Journal of Time Series Analysis, 1989Abstract. It is shown that a real‐valued discrete‐parameter Gaussian ARMA (p. q) model with q < p can be embedded in a real‐valued continuous‐parameter Gaussian ARMA(p', q') model with q' < p'. The problem of embedding a real‐valued discrete‐parameter Gaussian AR(p) into a real‐valued continuous‐parameter Gaussian AR(p) is also discussed.
He, S. W., Wang, J. G.
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Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181), 2002
We present some aspects of non-Gaussian H-ARMA models. After recalling that an H-ARMA process is obtained by passing an ARMA process through a Hermite polynomial nonlinearity, we describe the theoretical analysis of their cumulants and cumulant spectra. The main advantage of this kind of model is that the cumulant structure of the output can be deduced
David Declercq, Patrick Duvaut
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We present some aspects of non-Gaussian H-ARMA models. After recalling that an H-ARMA process is obtained by passing an ARMA process through a Hermite polynomial nonlinearity, we describe the theoretical analysis of their cumulants and cumulant spectra. The main advantage of this kind of model is that the cumulant structure of the output can be deduced
David Declercq, Patrick Duvaut
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DIFFERENTIAL GEOMETRY OF ARMA MODELS
Journal of Time Series Analysis, 1990Abstract.A general approach for the development of a statistical inference on autoregressive moving‐average (ARMA) models is presented based on geometric arguments. ARMA models are characterized as members of the curved exponential family. Geometric properties of ARMA models are computed and used to suggest parameter transformations that satisfy ...
Ravishanker, Nalini +2 more
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A recursive procedure for ARMA modeling
IEEE Transactions on Acoustics, Speech, and Signal Processing, 1985This paper presents a two-part fast recursive algorithm for ARMA modeling. The algorithm first obtains estimates of the p autoregressive coefficients from a set of p extended Yule-Walker equations. An exact recursive lattice algorithm for this estimator is then derived.
Randolph L. Moses +2 more
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ARMA Model for Revenue Prediction
Proceedings of the 11th International Conference on Advances in Information Technology, 2020For every country in over the world, tax revenues appear to be the main engines contributing to the growth momentum. The prediction of tax revenues is one of the main challenges of the Myanmar Internal Revenue Department. It is not easy to get an accurate prediction of the tax revenues of the coming financial year.
Thura Zaw, Swe Swe Kyaw, Aung Nway Oo
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Arma models with bilinear innovations
Communications in Statistics. Stochastic Models, 1999Summary: It is well known that any purely non-deterministic stationary process \((X_t)\) with finite variance can be written as an infinite moving average in terms of its innovation process. This property is widely used in the linear methods of estimation and prediction of time series but these methods may give poor results when the innovations are not
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ESTIMATION OF SPATIAL ARMA MODELS
Australian Journal of Statistics, 1992SummarySpatial ARMA models are considered using the nonsymmetric half plane ordering on a lattice of data. A method is given for the estimation of the orders and the coefficients of such models under an identifiability condition and the condition that the beat linear predictor is the best predictor in the mean square sense.
Huang, D., Anh, V. V.
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State representations of ARMA-models
International Journal of Control, 2010A state representation of an arbitrary ARMA-model is computed explicitly. It is shown then that every ARMA-model is homotopy equivalent to its state representation, and that two state models are homotopy equivalent if and only if they are similar.
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Modelling and Forecasting with ARMA Processes
1996The 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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