In recent years, Bitcoin has become the most widely used blockchain platform in business and finance. The goal of this work is to find a viable prediction model that incorporates and perhaps improves on a combination of available models. Among the techniques utilized in this paper are exponential smoothing, ARIMA, artificial neural networks (ANNs) models, and prediction combination models. The study's most obvious discovery is that artificial intelligence models improve the results of compound prediction models. The second key discovery was that a strong combination forecasting model that responds to the multiple fluctuations that occur in the bitcoin time series and Error improvement should be used. Based on the results, the prediction acc

الخلاصة

اهتم الفكر السياسي في القرنين الاخيرين بدراسة الطبقات على نحو غير مسبوق, واصبح موضوع التحليل الطبقي المعني بالطبقات من حيث تعريفها, وتحديد موقعها في السلم الاجتماعي, فضلاً عن نوعية العلاقة بين شرائحها وفئاتها المختلفة من حيث الصراع والتناغم, المادة الرئيسة والموضوع الاكثر اهمية في دراسات الفكر السياسي والاجتماعي.ومن بين الطبقات, احتلت الطبقة الوسطى مكا

Interval methods for verified integration of initial value problems (IVPs) for ODEs have been used for more than 40 years. For many classes of IVPs, these methods have the ability to compute guaranteed error bounds for the flow of an ODE, where traditional methods provide only approximations to a solution. Overestimation, however, is a potential drawback of verified methods. For some problems, the computed error bounds become overly pessimistic, or integration even breaks down. The dependency problem and the wrapping effect are particular sources of overestimations in interval computations. Berz (see [1]) and his co-workers have developed Taylor model methods, which extend interval arithmetic with symbolic computations. The latter is an ef

. In recent years, Bitcoin has become the most widely used blockchain platform in business and finance. The goal of this work is to find a viable prediction model that incorporates and perhaps improves on a combination of available models. Among the techniques utilized in this paper are exponential smoothing, ARIMA, artificial neural networks (ANNs) models, and prediction combination models. The study's most obvious discovery is that artificial intelligence models improve the results of compound prediction models. The second key discovery was that a strong combination forecasting model that responds to the multiple fluctuations that occur in the bitcoin time series and Error improvement should be used. Based on the results, the prediction a

Abstract

Objective / Purpose: Online social relationships through the emergence of Web 2.0 applications have become a new trend for researchers to study the behavior of consumers to shop online, as well as social networking sites are technologies that opened up opportunities for new business models. Therefore, a new trend has emerged, called social trade technology. In order to understand the behavioral intentions of the beneficiaries to adopt the technology of social trade, the current research aims at developing an electronic readiness framework and UTAUT model to understand the beneficiary's adoption of social trade technology.

Design/ methodology/ Approach: To achieve the obje

The use of Bayesian approach has the promise of features indicative of regression analysis model classification tree to take advantage of the above information by, and ensemble trees for explanatory variables are all together and at every stage on the other. In addition to obtaining the subsequent information at each node in the construction of these classification tree. Although bayesian estimates is generally accurate, but it seems that the logistic model is still a good competitor in the field of binary responses through its flexibility and mathematical representation. So is the use of three research methods data processing is carried out, namely: logistic model, and model classification regression tree, and bayesian regression tree mode

         The main objective of this study is to measure the Impact of global financial crisis on some indicators of the Saudi Arabia's economy using the Mendel-Fleming model, the importance of the study applied by focusing on the theme of general equilibrium in the face of fluctuations in the global economy. Study used a descriptive approach and the methodology of econometrics to construct the model. Study used Eviews Program for data analysis. The Data was collected from the Saudi Arabian Monetary Agency, for the period (1997-2014).Stationery of the variables was checked by Augmented Dickey-Fuller (ADF) and Phillips Perron (PP) unit roots tests. And also the co-integration

In this work, the nuclear electromagnetic moments for the ground and low-lying excited states for sd shell nuclei have been calculated, resulting in a revised database with 56 magnetic dipole moments and 41 electric quadrupole moments. The shell model calculations are performed for each sd isotope chain, considering the sensitivity of changing the sd two-body effective interactions USDA, USDE, CWH and HBMUSD in the calculation of the one-body transition density matrix elements. The calculations incorporate the single-particle wave functions of the Skyrme interaction to generate a one-body potential in Hartree–Fock theory to calculate the single-particle matrix elements. For most sd shell nuclei, the experimental data are well rep