Application of Neural Networks for Predicting the Exchange Rate of the Iraqi Dinar Against the US Dollar and Comparison with the Box-Jenkins Method for Time Series 2015-2022
DOI:
https://doi.org/10.62933/egnts624Abstract
Recently, there have been changes in the exchange rate of the Iraqi dinar against foreign currencies, which are considered important financial indicators that affect the labor market and the currency exchange market. In order to monitor the changes in the exchange rate of the Iraqi dinar against the US dollar and to anticipate future stages and the direction of the exchange rate changes, the aim of the research is to predict the exchange rate of the Iraqi dinar against the US dollar for the coming years by applying the Box-Jenkins methodology and neural networks. This is done to compare the traditional predictive models, such as ARIMA, with the neural network model, which demonstrated its prediction accuracy by reducing the Mean Squared Error (MSE) through training the network, selecting the appropriate model, and choosing the best architecture to represent the time series.
The study included a time series representing the exchange rates of the Iraqi dinar against the US dollar from January to December for the years 2015-2022. The data was sourced from the 2021/2022 annual statistical group issued by the Central Statistical Organization of Iraq, with data from the Central Bank of Iraq. For the analysis of the time series, the GRETL statistical program was used, and the Matlab R 2019b program was utilized for forecasting when using neural networks.
References
[1] Ahmed, Ammar Shihab (2017), "Using Time Series Models to Forecast Iraq's Oil Exports for the Years (2016-2017)," Baghdad College of Economic Sciences Journal, Issue 52.
[2] Ahmed Mehaoua,Nabil Tabbane,& Sami Tabbane (2004),”Autoregressive,Moving Average and Mixed Autoregressive – Moving Average Processes For Forecasting Qos In Ad Hoc Networks For Real- Time service support”,University of Versailles, France.
[3] Al-Awadi, Haider Abdullah (2015), "Using Some Time Series Methods to Forecast Births in Iraq," Higher Diploma in Applied Statistics, College of Administration and Economics, University of Baghdad.
[4] Al-Hadithi, Issam Maulood, 1993, "A Study on Forecasting the Production of Yellow Corn in Iraq," Master's Thesis in Statistics, University of Baghdad, College of Administration and Economics.
[5] Ali Basim Majid (2019), "Using Time Series to Forecast Consumer Price Indexes in Iraq for the Period (2018-2022)," Higher Diploma in Applied Statistics, College of Administration and Economics, University of Baghdad.
[6] Al-Suhail, Rabab Ali Abdul Rahman (2016), "Using Box-Jenkins Models to Forecast the Production Quantity of Some Plant and Animal Products in Baghdad Governorate," Higher Diploma in Applied Statistics, College of Administration and Economics, University of Baghdad.
[7] Bari, Adnan, and Majid Abdul Rahman, 2002, "Statistical Forecasting Methods, Part One," Department of Statistics and Operations Research, Volume One, King Saud University.
[8] Cavanaugh, J.E. , & Neath,A.A.(2019). The Akaike information criterion: Background, derivation, properties, application, interpretation, and refinements. Wiley Interdisciplinary Reviews: Computational Statistics,e1460.
[9] C.H.wu ,RD.soto ,p.p.valko ,A.M.Bubela ,(2000) , “Non- Parametric regression and Neural-Network infill drilling recovery models for carbonater reservoirs”,computers Geosciences ,26,pp.975-987 .
[10] Darwish, Marwan (2018), "Using the Box-Jenkins Methodology to Forecast Cash Flows in Palestinian Banks: A Case Study of Palestine Bank," Journal of Al-Quds Open University for Administrative and Economic Research, Volume 3, Issue 9.
[11] Douglas C.Montgmery,chery L.Jennings,& Murat Kulahci(2008),”Introduction to Time Series Analysis and Forecasting’,Library of Congress,united states of America.
[12] Da Silva, I.N.,Hernane Spatti,D.,Andrade Flauzino,R.,Liboni, L.H.B.,dos Reis Alves,S.F.(2017).Artificial Neural Network Arechitectures and Training Processes.In:Artificial Neural Networks.Springer,Cham.
[13] Hala Gabreel Mohamed (2017), "Using Time Series Analysis to Forecast Malaria Cases: A Case Study," Master's Thesis, Department of Applied Statistics, University of Al-Jazira, Sudan.
[14] Ibrahim, Bassam Younis, 2004, "Forecasting Temperature in Khartoum State Using Box-Jenkins Models for Time Series," Sudan University, published in the Sudan Journal of Science and Culture, Volume 5, Issue 2.
[15] Imad Yagoub Hamid , (2011), “Use of Box – Jenkins and Artificial Neural Networks models in Time Series prediction for Sudanese Agricultural sector ”, The Third International Conference for Arab Statisticians , Isso,No,501.
[16] Jumaa, Ahlam Ahmed and Hussein, Hala Fadel (2013), "Analysis of the Characteristics of Time Series Models for Oil Sector Data in Iraq for the Period (1958-2008)," Paper presented at the Eighth Scientific Conference, University of Karbala, College of Administration and Economics.
[17]Karlik,B.and Olgac,A.V.,2011.performance analysis of various activation functions in generalized MLP architectures of neural networks.International Journal of Artificial Intelligence and Expert Systems,1(4),pp.111-122.
[18] Majd Namaa,(2023),” Comparison Between Traditional Models and the Use of Artificial Intelligence Models (Neural Networks) to Predict Tobacco Production in Lattakia-Syria”,Syrian Journal of Agricultural Research – SJAR 10(1) :, February ,p p. 142 – 155 .
WWW.alepposoft.com ” Neural Network “19 -
[20] Peter J.Brockwell, Richard A. Davis,Introduction to Time Series and Forecasting, Second Edition, springer-verlag,New York, USA,2002,p1.-18.10.4137/BECB.S31601.
[21] Pouliakis,A& Karakitsou,E&others.2016.”Artificial Neural Networks as Decision Support Tools in Cytopathology:past present and future “.Biomed Eng Compute Biol.2016.1
[22] Sadia Abdul Kareem Ta'ma (2012), "Using Time Series to Forecast the Number of Malignant Tumor Cases in Anbar Governorate," Al-Anbar University Journal of Economic and Administrative Sciences, Volume 4, Issue 8.
[23] Saleh, Abbas Mahdi (2017), "Forecasting the Number of Live Births and Constructing Life Tables for Diyala Governorate," Higher Diploma in Applied Statistics, Department of Statistics, College of Administration and Economics, University of Baghdad.
[24] Sharma,S.and Athaiya, A.,2017.Activation functions in neural networks.Towards Data Sci,6(12),pp.310-316.
[25] Salioua Rahad and Matar Dhafir (2019),” Comparison of Prediction Performance Between Some Artificial Neural Networks and the Box-Jenkins Methodology”, Iraqi Journal of Statistical Sciences, Issue 28, pp 51-76.
[26] Spyros Makridakis,& Michele Hibon(1997),”Arma Mosels and The Box-Jenkins Methodology”,Insead,France.
[27] William W.Hsieh and Vancouver ,(2004),“Nonliner Multivariate and Time series Analysis By Neural Network methods ”March.18, pp.1-25 . www.ocgy.ubc.ca/~william/Pubs/Rev.Geop.pdf
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