The monthly time series of the Total Suspended Solids (TSS) concentrations in Euphrates River at Nasria was analyzed as a time series. The data used for the analysis was the monthly series during (1977-2000).

The series was tested for nonhomogenity and found to be nonhomogeneous. A significant positive jump was observed after 1988. This nonhomogenity was removed using a method suggested by Yevichevich (7). The homogeneous series was then normalized using Box and Cox (2) transformation. The periodic component of the series was fitted using harmonic analyses, and removed from the series to obtain the dependent stochastic component. This component was then modeled using first order autoregressive model (Markovian chain). The above a

Many academics have concentrated on applying machine learning to retrieve information from databases to enable researchers to perform better. A difficult issue in prediction models is the selection of practical strategies that yield satisfactory forecast accuracy. Traditional software testing techniques have been extended to testing machine learning systems; however, they are insufficient for the latter because of the diversity of problems that machine learning systems create. Hence, the proposed methodologies were used to predict flight prices. A variety of artificial intelligence algorithms are used to attain the required, such as Bayesian modeling techniques such as Stochastic Gradient Descent (SGD), Adaptive boosting (ADA), Decision Tre

Many academics have concentrated on applying machine learning to retrieve information from databases to enable researchers to perform better. A difficult issue in prediction models is the selection of practical strategies that yield satisfactory forecast accuracy. Traditional software testing techniques have been extended to testing machine learning systems; however, they are insufficient for the latter because of the diversity of problems that machine learning systems create. Hence, the proposed methodologies were used to predict flight prices. A variety of artificial intelligence algorithms are used to attain the required, such as Bayesian modeling techniques such as Stochastic Gradient Descent (SGD), Adaptive boosting (ADA), Deci

In this paper, some estimators of the unknown shape parameter and reliability function  of Basic Gompertz distribution (BGD) have been obtained, such as MLE, UMVUE, and MINMSE, in addition to estimating Bayesian estimators under Scale invariant squared error loss function assuming informative prior represented by Gamma distribution and non-informative prior by using Jefferys prior. Using Monte Carlo simulation method, these estimators of the shape parameter and R(t), have been compared based on mean squared errors and integrated mean squared, respectively

Often there is no well drilling without problems. The solution lies in managing and evaluating these problems and developing strategies to manage and scale them. Non-productive time (NPT) is one of the main causes of delayed drilling operations. Many events or possibilities can lead to a halt in drilling operations or a marginal decrease in the advancement of drilling, this is called (NPT). Reducing NPT has an important impact on the total expenditure, time and cost are considered one of the most important success factors in the oil industry. In other words, steps must be taken to investigate and eliminate loss of time, that is, unproductive time in the drilling rig in order to save time and cost and reduce wasted time. The data of

The volatility of the financial markets and the oil market plays a major role in influencing macroeconomic activity, as well as the high interaction between the both markets and the remarkable sensitivity to their each other fluctuations which cause the undesirable impact on other economic sectors as an expected result due the mentioned interaction.

The study aimed to analyze the relationship between the volatility of the major US market indices represented by the DJIA index, S & P500, due to their comprehensiveness of the financial market, as they summarize the performance of the entire US market which is the largest economy in the world, as well as the difference in the calculation mechanism, and oi

The Artificial Neural Network methodology is a very important & new subjects that build's the models for Analyzing, Data Evaluation, Forecasting & Controlling without depending on an old model or classic statistic method that describe the behavior of statistic phenomenon, the methodology works by simulating the data to reach a robust optimum model that represent the statistic phenomenon & we can use the model in any time & states, we used the Box-Jenkins (ARMAX) approach for comparing, in this paper depends on the received power to build a robust model for forecasting, analyzing & controlling in the sod power, the received power come from

Abstract

That Iraq's dependence on the revenues of the oil product in financing its development programs and growth rates , Making the economy affected by external forces represented by fluctuations in crude oil prices in the global market, Which is directly reflected on the performance and efficiency of the Iraqi economy.

The study adopted its objectives to analyze the time series for the period (1988 - 2015) through the use of standard and statistical methods, Four standard models were estimated to reach those targets, Where the results of the stability test showed instability of most variables at their original level, But to achieve stability when taking the first differences, While the result