When we were fitting ARMA models to the data, we first looked at the sample autocovariance or autocorrelation function and we then tried to find the ARMA model whose theoretical acf matched with the sample acf. Now the sample autocovariance function is a nonparametric estimate of the theoretical autocovariance function of the process. In other words, we first estimated γ(h) nonparametrically by...

Abstract – Analysis of time series data can involve the inversion of large covariance matrices. For the class of ARMA (p, q) processes there are no exact explicit expressions for these inverses, except for the MA (1) process. In practice, the sample covariance matrix can be very large and inversion can be computationally time consuming and so approximate explicit expressions for the inverse are...

This paper presents a method to improve implementation accuracy of a recently proposed CORDIC ARMA lattice filter. Since the CORDIC ARMA lattice filter algorithm has a problem in its shift sequence, i t cannot implement a lattice filter accurately. Therefore, in this paper we apply the shift sequence proposed by Walther without the problem to the CORDIC ARMA lattice filter, and then we realize ...

Autoregressive moving average (ARMA) models are useful statistical tools to examine the dynamical characteristics of ecological time-series data. Here, we illustrate the utility and challenges of applying ARMA (p,q) models, where p is the dimension of the autoregressive component of the model, and q is the dimension of the moving average component. We focus on parameter estimation and model sel...

We report armA in an Escherichia coli pig isolate from Spain. The resistance gene was borne by self-transferable IncN plasmid pMUR050. Molecular analysis of the plasmid and of the armA locus confirmed the spread of this resistance determinant.

The renewable energies prediction and particularly global radiation forecasting is a challenge studied by a growing number of research teams. This paper proposes an original technique to model the insolation time series based on combining Artificial Neural Network (ANN) and Auto-Regressive and Moving Average (ARMA) model. While ANN by its non-linear nature is effective to predict cloudy days, A...

The 16S rRNA methyltransferase ArmA is a worldwide emerging determinant that confers high-level resistance to most clinically relevant aminoglycosides. We report here the identification and characterization of a multidrug-resistant Salmonella enterica subspecies I.4,12:i:- isolate recovered from chicken meat sampled in a supermarket on February 2009 in La Reunion, a French island in the Indian ...

Often exploration seismic data lacks low and high frequency band signals. The low frequency information provides crucial information about the mean model. Thus, estimation of absolute models using inversion schemes is difficult in case of band limited seismic data. We present a new method to synthesize initial model for inversion of seismic data using autoregressive and moving average modeling....

Since the empirical characteristic function (ECF) is the Fourier transform of the empirical distribution function, it retains all the information in the sample but can overcome difficulties arising from the likelihood. This paper discusses an estimation method via the ECF for strictly stationary processes. Under some regularity conditions, the resulting estimators are shown to be consistent and...

In this paper we introduce a robust feature extractor, dubbed as Modified Function Cepstral Coefficients (MODFCC), based on gammachirp filterbank, Relative Spectral (RASTA) and Autoregressive Moving-Average (ARMA) filter. The goal of this work is to improve the robustness of speech recognition systems in additive noise and real-time reverberant environments. In speech recognition systems Mel-Fr...