The energy sector which includes gas and oil is concerned to explore develop refined it’s a multitrillion business. As crude very important source of energy, it has valuable impact on country’s economic growth, national security, social stability. Therefore, accurately predicting the price volatility topic research still, challenge for researchers forecast prices. this study conducted address s...

Traditionally, the autoregressivemoving average (ARMA)model has been one of themost widely used linear models in time series prediction. Recent research activities in forecasting with artificial neural networks (ANNs) suggest that ANNs can be a promising alternative to the traditional ARMA structure. These linear models and ANNs are often compared with mixed conclusions in terms of the superior...

Extended Thermodynamics of dense gases is characterized by two hierarchies of field equations, which allow one to overcome some restrictions on the generality of the previous models. This idea has been introduced by Arima, Taniguchi, Ruggeri and Sugiyama. In the case of a 14-moment model, they have found the closure of the balance equations up to second order with respect to equilibrium. Here, ...

Extensive sea ice over Arctic regions is largely involved in heat, moisture, and momentum exchanges between the atmosphere and ocean. Some previous studies have been conducted to develop statistical models for the status of Arctic sea ice and showed considerable possibilities to explain the impacts of climate changes on the sea ice extent. However, the statistical models require improvements to...

This paper presents an overview of and introduction to some of the standard time series modeling and forecasting techniques as implemented in SAS with PROC ARIMA and PROC AUTOREG, among others. Examples are presented to illustrate the concepts. In addition to a few initial ARIMA examples, more sophisticated modeling tools will be addressed. Included will be regression models with time series er...

This paper presents the use of times series AutoRegressive Integrated Moving Average ARIMA(p,d,q) model with interventions, and neural network back-propagation model in analyzing the behavior of sales in a medium size enterprise located in Rio Grande do Sul Brazil for the period January 1984 – December 2000. The forecasts obtained using the neural network back-propagation model were found to be...

We briefly review various mappings of fermion pairs to bosons, including those based on mapping operators, such as Belyaev-Zelevinskii, and those on mapping states, such as Marumori; in particular we consider the work of Otsuka-Arima-Iachello, aimed at deriving the Interacting Boson Model. We then give a rigorous and unified description of state-mapping procedures which allows one to systematic...

Atrial Fibrillation (AF) is the most common cardiac arrhythmia. It naturally tends to become a chronic condition, and chronic Atrial Fibrillation leads to an increase in the risk of death. The study of the electrocardiographic signal, and in particular of the tachogram series, is a usual and effective way to investigate the presence of Atrial Fibrillation and to detect when a single event start...

The main objective of this paper is to show potential usefulness of the combination of autoregressive integrated moving average (ARIMA) models and logistic regression with automatic model selection (see our work presented at SUGI’28 and SUGI’29.) Timeseries analysis with ARIMA provides only one perspective of the information in the surveillance data (i.e. the number of patients as a function of...