ARMA model order and parameter estimation using genetic algorithms

Multichannel ARMA Parameter Estimation

Using a perturbation matrix, we introduced a hyperplane used to define a generalized null-spectrum, based on both the signal and noise subspaces, while the MUSIC and Min-Norm null-spectra are defined based only on the noise subspace. With the generalized nullspectrum, we derived the upper and lower bounds of a class of the generalized null-spectrum, called the maximum and minimum null-spectra, ...

Parameter Estimation By Genetic Algorithms

Most of the existing parameter estimation methods require much auxiliary information and is not easy to handle for practical applications. In this paper , we use genetic algorithms, which are becoming an important tool for optimization problems, to estimate parameters in nonlinear regression models. The computational experiments indicate that this genetic based method is useful, eeective and ad...

Model Identi cation and Parameter Estimation of ARMA Models by Means of Evolutionary Algorithms

The eld of time series analysis and forecasting methods has signi cantly changed in the last decade due to the in uence of new knowledge in non-linear dynamics. New methods such as arti cial neural networks replaced traditional approaches which usually were appropriate for linear models only. Nevertheless, there are still applications where accurate estimations of linear processes, such as ARMA...

Model identification and parameter estimation of ARMA models by means of evolutionary algorithms

ARMA model parameter estimation based on the equivalent MA approach

The paper investigates the relation between the parameters of an autoregressive moving average (ARMA) model and its equivalent moving average (EMA) model. On the basis of this relation, a new method is proposed for determining the ARMA model parameters from the coefficients of a finite-order EMA model. This method is a three-step approach: in the first step, a simple recursion relating the EMA ...