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 …

Artificial neural networks (ANNs) are flexible computing frameworks and universal
approximates that can be applied to a wide range of time series forecasting problems with a …

The goal of this paper is to predict electrical energy consumption using nonlinear
autoregressive (NAR) models. The practical section contains historical data on Iraq's annual …

Youth unemployment significantly impacts the socio-economic stability and growth of
developing countries, particularly Iraq. This study aims to predict the youth unemployment …

Forecasting economic indicators like Gross Domestic Product (GDP) is crucial for planning
and decision-making by policymakers, investors, and businesses. Traditional econometric …

This paper aims to enhance the accuracy and reliability of gas production predictions for Iraq
until 2030 by comparing traditional time series methods (ARIMA) with a hybrid artificial …

This research enhances demographic forecasting by utilizing Support Vector Regression
(SVR), a specialized implementation of Support Vector Machine (SVM), to precisely …

This paper presents an innovative approach to economic forecasting by integrating Artificial
Neural Networks (ANN) and Wavelet Analysis (WANN) into a hybrid model, aimed at …

In many developing countries export earnings account for a large percentage of this paper
provides a comparative examination of two forecasting models, namely the Autoregressive …

The purpose of this study is to investigate the effectiveness of increasing the number of
nodes in the input layer of an artificial neural network (ANN) to improve its output and …