The purpose of this paper is to model and forecast the white oil during the period (2012-2019) using volatility GARCH-class. After showing that squared returns of white oil have a significant long memory in the volatility, the return series based on fractional GARCH models are estimated and forecasted for the mean and volatility by quasi maximum likelihood QML as a traditional method. While the competition includes machine learning approaches using Support Vector Regression (SVR). Results showed that the best appropriate model among many other models to forecast the volatility, depending on the lowest value of Akaike information criterion and Schwartz information criterion, also the parameters must be significant. In addition, the residuals don’t have the serial correlation and ARCH effect, as well as these models, should have a higher value of log-likelihood and SVR-FIGARCH models managed to outperform FIGARCH models with normal and student’s t distributions. The SVR-FIGARCH model exhibited statistical significance and improved accuracy obtained with the SVM technique. Finally, we evaluate the forecasting performance of the various volatility models, and then we choose the best fitting model to forecast the volatility for each series, depending on three forecasting accuracy measures RMSE, MAE, and MAPE.
The Turonian-Lower Companian succession at Majnoon Oil Field is represented by the Khasib, Tanuma, and Saadi formations. Four major paleoenvironments were recognized within the studied succession, there are: Shallow open marine environment, shoal environment, deep marine environment, and basinal environment. They reflect deposition on a carbonate platform of homoclinal ramp setting. The studied succession represents two second order supersequences (A) and (B). Supersequence (A) includes both the Khasib and Tanuma formations. The Saadi Formation represents cycle (B). These second order cycles can be divided each into two third order cycles, This subdivision may reflect the effect of eustacy being the major controlling factor of cycles dev
... Show MoreThis Research Tries To Investigate The Problem Of Estimating The Reliability Of Two Parameter Weibull Distribution,By Using Maximum Likelihood Method, And White Method. The Comparison Is done Through Simulation Process Depending On Three Choices Of Models (?=0.8 , ß=0.9) , (?=1.2 , ß=1.5) and (?=2.5 , ß=2). And Sample Size n=10 , 70, 150 We Use the Statistical Criterion Based On the Mean Square Error (MSE) For Comparison Amongst The Methods.
In this work; Silicon dioxide (SiO2) were fabricated by pulsed
laser ablation (PLA). The electron temperature was calculated by
reading the data of I-V curve of Langmuir probe which was
employed as a diagnostic technique for measuring plasma properties.
Pulsed Nd:YA Glaser was used for measuring the electron
temperature of SiO2 plasma plume under vacuum environment with
varying both pressure and axial distance from the target surface. The
electron temperature has been measured experimentally and the
effects of each of pressure and Langmuir probe distance from the
target were studied. An inverse relationship between electron
temperature and both pressure and axial distance was observed.
In this article, performing and deriving te probability density function for Rayleigh distribution is done by using ordinary least squares estimator method and Rank set estimator method. Then creating interval for scale parameter of Rayleigh distribution. Anew method using is used for fuzzy scale parameter. After that creating the survival and hazard functions for two ranking functions are conducted to show which one is beast.
Visual analytics becomes an important approach for discovering patterns in big data. As visualization struggles from high dimensionality of data, issues like concept hierarchy on each dimension add more difficulty and make visualization a prohibitive task. Data cube offers multi-perspective aggregated views of large data sets and has important applications in business and many other areas. It has high dimensionality, concept hierarchy, vast number of cells, and comes with special exploration operations such as roll-up, drill-down, slicing and dicing. All these issues make data cubes very difficult to visually explore. Most existing approaches visualize a data cube in 2D space and require preprocessing steps. In this paper, we propose a visu
... Show MoreThe using of the parametric models and the subsequent estimation methods require the presence of many of the primary conditions to be met by those models to represent the population under study adequately, these prompting researchers to search for more flexible parametric models and these models were nonparametric, many researchers, are interested in the study of the function of permanence and its estimation methods, one of these non-parametric methods.
For work of purpose statistical inference parameters around the statistical distribution for life times which censored data , on the experimental section of this thesis has been the comparison of non-parametric methods of permanence function, the existence
... Show MoreSamples of Iraqi bentonitic sediments, representing local montmorillonite brought from Traifawi region near the Syrian border. Mineralogical the samples were characterized as low grade of Ca-smectite, particle size, chemical analysis, XRD, and BET surface area analyses of the samples were carried out to examine the structure of bentonite before and after acid activation. The goal is to prepare a bleaching earth for edible oil production. Iraqi Bentonite was beneficiated and activated by series of physical and chemical steps, using 4N & 6N concentration of hydrochloric acid and at a temperature of 70-80 ° C. Surface area and pore volume of the samples were determined to assess the bleaching power
Baker's Yeast is an important additive among the substances, which improves bred quality, thus, a consideration has been made to study the conditions and parameters that affecting the production of the yeast in a batch fermenter experimentally and theoretically. Experimental runs were implemented in a 12-liter pilot-scale fermenter to predict the rate of growth and other parameters such as amount of additive consumed and the amount of heat generated. The process is modeled and performed using a computer programming prepped for this purpose, the model gave a good agreement comparing to the experimental work specially in the log phase.