The object of this paper is to study the asymptotic dependence structure of the linear time series models with infinitely divisible innovations by the use of their characteristic functions. Autoregressive moving-average (ARMA) models and fractional autoregressive integrated moving-average (FARIMA) models are analyzed. As examples of infinitely divisible innovations, the class of radially absolu...

Predicting future probable values of model parameters, is an essential pre-requisite for assessing model decision reliability in an uncertain environment. Scenario Analysis is a methodology for modelling uncertainty in water resources management modelling. Uncertainty if not considered appropriately in decision making will decrease reliability of decisions, especially in long-term planning. One...

Many of the existing autoregressive moving average (ARMA) forecast models are based on one main factor. In this paper, we proposed a new two-factor first-order ARMA forecast model based on fuzzy fluctuation logical relationships of both a main factor and a secondary factor of a historical training time series. Firstly, we generated a fluctuation time series (FTS) for two factors by calculating ...

The prediction of solar radiation has a significant role in several fields such as photovoltaic (PV) power production and micro grid management. interest is increasing nowadays so efficient can greatly improve the performance these different applications. This paper presents novel approach which combines two models, Auto Regressive Moving Average (ARMA) Nonlinear with eXogenous input (NARX). ch...

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 ...

High-level resistance to aminoglycosides produced by 16S rRNA methylases in Enterobacteriaceae isolates was investigated. The prevalences of armA in Escherichia coli, Klebsiella pneumoniae, and Enterobacter cloacae were 0.6%, 3.0%, and 10%, respectively. rmtB was more prevalent than armA. Pulsed-field gel electrophoresis patterns indicated that armA and rmtB have spread horizontally and clonally.

In this paper, a comparison study is presented on artificial intelligence and time series models in 1-hour-ahead wind speed forecasting. Three types of typical neural networks, namely adaptive linear element, multilayer perceptrons, and radial basis function, and ARMA time series model are investigated. The wind speed data used are the hourly mean wind speed data collected at Binalood site in I...

Abstract Power system dispatch benefits from accurate wind power predictions. To increase the prediction precision for power, this paper proposes a combined model predicting short-term based on autoregressive moving average-gated recurrent unit (ARMA-GRU). Firstly, we build ARMA and GRU respectively to predict power. Then optimize model’s weights by quantum particle swarm algorithm (QPSO). Fina...

Sir, Methylation of 16S ribosomal RNA (rRNA) mediated by 16S rRNA methylase, which confers high-level aminoglycoside resistance (MICs .1024 mg/L) in Enterobacteriaceae, has recently emerged as a major medical problem worldwide. Until now, seven plasmid-encoded 16S rRNA methylases, ArmA, RmtA, RmtB, RmtC, RmtD, RmtE and NpmA, have been reported in various clinical Gram-negative isolates in multi...

We study how to perform model selection for time series data where millions of candidate ARMA models may be eligible for selection.We propose a feasible computingmethod based on theGibbs sampler. By thismethodmodel selection is performed through a random sample generation algorithm, and given amodel of fixed dimension the parameter estimation is done through the maximum likelihood method. Our m...