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Applying some hybrid models for modeling bivariate time series assuming different distributions for random error with a practical application
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Abstract

  Bivariate time series modeling and forecasting have become a promising field of applied studies in recent times. For this purpose, the Linear Autoregressive Moving Average with exogenous variable ARMAX model is the most widely used technique over the past few years in modeling and forecasting this type of data. The most important assumptions of this model are linearity and homogenous for random error variance of the appropriate model. In practice, these two assumptions are often violated, so the Generalized Autoregressive Conditional Heteroscedasticity (ARCH) and (GARCH) with exogenous variables (GARCHX) are applied to analyze and capture the volatility that occurs in the conditional variance of a linear model. Since time series observations rarely have linear or nonlinear components in nature or usually included together. Therefore, the main purpose of this paper is to employ the hybrid model technique according to Zhang methodology for hybrid models to combine the linear forecasts of the best linear model of ARMAX models and the nonlinear forecasts of the best nonlinear models of (ARCH, GARCH & GARCHX) models and thus increase the efficiency and accuracy of performance forecasting future values of the time series.

This paper is concerned with the modeling and building of the hybrid models (ARMAX-GARCH) and (ARMAX-GARCHX), assuming three different random error distributions: Gaussian distribution, Student-t distribution, as well as the general error distribution and the last two distributions were applied for the purpose of capturing the characteristics of heavy tail distributions which have a Leptokurtic characteristic compared to the normal distribution. This research adopted a modern methodology in estimating the parameters of the hybrid model namely the (two-step procedure) methodology. In the first stage, the parameters of the linear model were estimated using three different methods: The Ordinary Least Squares method (OLS), the Recursive Least Square Method with Exponential Forgetting Factor (RLS-EF), and the Recursive Prediction Error Method (RPM). In the second stage, the parameters of the nonlinear model were estimated using the MLE method and employing the numerical improvement algorithm (BHHH algorithm).

 

 

 

The hybrid models have been applied for modeling the relationship between the exogenous time series represented by the exchange rate and the endogenous time series represented by the unemployment rate in the USA for the period from (January 2000 to December 2017 i.e. 216 observations), and also the out-of-sample forecasts of unemployment rate in the last twelve values of (2018). The forecasting performance of the hybrid models and the competing individual model was also evaluated using the loss function accuracy measures (MAPE), (MAE), and the robust (Q-LIKE). Based on statistical measurements, the results showed the hybrid models improved the accuracy and efficiency of the single model. () hybrid model error whose conditional variance follows a GED distribution is the optimal model in modeling the bivariate time series data under study and more efficient in the forecasting process compared with the individual model and the hybrid model. This is due to having the lowest values for accuracy measures. Different software packages (MATLAB (2018a), SAS 9.1, R 3.5.2 and EViews 9) were used to analyze the data under consideration.

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Publication Date
Wed Feb 29 2012
Journal Name
Al-khwarizmi Engineering Journal
Design a Fault Tolerance for Real Time Distributed System
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This paper designed a fault tolerance for soft real time distributed system (FTRTDS). This system is designed to be independently on specific mechanisms and facilities of the underlying real time distributed system. It is designed to be distributed on all the computers in the distributed system and controlled by a central unit.

Besides gathering information about a target program spontaneously, it provides information about the target operating system and the target hardware in order to diagnose the fault before occurring, so it can handle the situation before it comes on. And it provides a distributed system with the reactive capability of reconfiguring and reinitializing after the occurrence of a failure.

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Publication Date
Thu Oct 20 2016
Journal Name
Sociological Methods & Research
Mean Monte Carlo Finite Difference Method for Random Sampling of a Nonlinear Epidemic System
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In this article, a numerical method integrated with statistical data simulation technique is introduced to solve a nonlinear system of ordinary differential equations with multiple random variable coefficients. The utilization of Monte Carlo simulation with central divided difference formula of finite difference (FD) method is repeated n times to simulate values of the variable coefficients as random sampling instead being limited as real values with respect to time. The mean of the n final solutions via this integrated technique, named in short as mean Monte Carlo finite difference (MMCFD) method, represents the final solution of the system. This method is proposed for the first time to calculate the numerical solution obtained fo

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Wed Jun 01 2022
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Applied Energy
Novel mathematical modeling, performance analysis, and design charts for the typical hybrid photovoltaic/phase-change material (PV/PCM) system
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Wed Jun 01 2022
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Publication Date
Mon Mar 08 2021
Journal Name
Baghdad Science Journal
using collocation method for solving differential equations with time lag
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in this paper the collocation method will be solve ordinary differential equations of retarted arguments also some examples are presented in order to illustrate this approach

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Publication Date
Wed Feb 01 2023
Journal Name
Periodicals Of Engineering And Natural Sciences (pen)
Bitcoin Prediction with a hybrid model
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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

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Scopus (10)
Scopus
Publication Date
Tue Feb 28 2023
Journal Name
Periodicals Of Engineering And Natural Sciences (pen)
Bitcoin Prediction with a hybrid model
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. 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

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Publication Date
Tue Mar 01 2022
Journal Name
International Journal Of Nonlinear Analysis And Applications
Semi-parametric regression function estimation for environmental pollution with measurement error using artificial flower pollination algorithm
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Artificial Intelligence Algorithms have been used in recent years in many scientific fields. We suggest employing flower pollination algorithm in the environmental field to find the best estimate of the semi-parametric regression function with measurement errors in the explanatory variables and the dependent variable, where measurement errors appear frequently in fields such as chemistry, biological sciences, medicine, and epidemiological studies, rather than an exact measurement. We estimate the regression function of the semi-parametric model by estimating the parametric model and estimating the non-parametric model, the parametric model is estimated by using an instrumental variables method (Wald method, Bartlett’s method, and Durbin

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Publication Date
Mon Feb 04 2019
Journal Name
Iraqi Journal Of Physics
Evaluated the level density for proton induced nuclear resonances in (P+48Ti) reaction using different models
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The experimental proton resonance data for the reaction P+48Ti have been used to calculate and evaluate the level density by employed the Gaussian Orthogonal Ensemble, GOE version of RMT, Constant Temperature, CT and Back Shifted Fermi Gas, BSFG models at certain spin-parity and at different proton energies. The results of GOE model are found in agreement with other, while the level density calculated using the BSFG Model showed less values with spin dependence more than parity, due the limitation in the parameters (level density parameter, a, Energy shift parameter, E1and spin cut off parameter, σc). Also, in the CT Model the level density results depend mainly on two parameters (T and ground state back shift energy, E0), which are app

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Publication Date
Thu Oct 18 2018
Journal Name
Lambert Academic Publishing
Mathematical Models For Contamination Soil
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