We propose autoregressive moving average (ARMA) and generalized autoregressive conditional heteroscedastic (GARCH) models driven by Asymmetric Laplace (AL) noise. The AL distribution plays, in the geometric-stable class, the analogous role played by the normal in the alpha-stable class, and has shown promise in the modeling of certain types of financial and engineering data. In the case of an A...

BACKGROUND Acinetobacter baumannii strains co-producing carbapenemase and 16S rRNA methylase are highly resistant to carbapenems and aminoglycosides. METHODS Ninety-three isolates of multidrug-resistant A. baumannii were obtained from an intensive care unit in a hospital in Vietnam. Antimicrobial susceptibility tests and whole genome sequencing were performed. Multilocus sequence typing and t...

This paper describes a new method for generating stationary integervalued time series from renewal processes. We prove that if the lifetime distribution of renewal processes is nonlattice and the probability generating function is rational, then the generated time series satisfy causal and invertible ARMA type stochastic difference equations. The result provides an easy method for generating in...

The purpose of this paper is to introduce a method of estimating parameters in nonnegative ARMA processes. The method is a generalization of the procedures which were derived for autoregressive and moving-average processes. The estimates are constructed in the form of minima of certain fractions or some functions of these minima. A theorem concerning the strong consistence of these estimates is...

In this research, monthly rainfall of Shiraz synoptic station from March 1971 to February 2016 was studied using different time series models by ITSM Software. Results showed that the ARMA (1,12) model based on Hannan-Rissanen method was the best model which fitted to the data. Then, to assess the verification and accuracy of the model, the monthly rainfall for 60 months (from March 2011 to Feb...

Linear in parameter models are quite widespread in process engineering, e.g. NAARX, polynomial ARMA models, etc. This paper proposes a new method for nonlinear structure selection for linear in parameter models. The method uses Genetic Programming (GP) to generate nonlinear input-output models represented in tree structure. The main idea of the paper is to apply Orthogonal Least Squares algorit...

Abstract Dairy sector is one of the fastest growing sectors in world with little global contributions from African countries and Nigeria particular. This study modelled forecast diary milk production Iwo its environs using different variants Autoregressive Moving Average (ARMA) models. Data used this comprised daily between 26th May, 2021 31st 2022 as obtained Bowen University collection centre...

In recent decades, the momentum of global environmental protection has culminated in the Kyoto Agreement of 1998, placing the limelight on “green” issues. This paper argues that the protection of environmental systems involves a fragile balance between the costs of environment preservation and the profit motivations of industrialists. In particular, one of the issues that needs to be addressed ...

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. Such models can be obtained by driving autoregressive moving average (ARMA) processes wi...

The problem we tackle concerns forecasting time series in financial markets. AutoRegressive Moving-Average (ARMA) methods and computational intelligence have also been used to tackle this problem. We propose a novel method for time series forecasting based on a hybrid combination of ARMA and Gene Expression Programming (GEP) induced models. Time series from financial domains often encapsulate d...