US Dollar/IQ Dinar Currency Exchange Rates Time Series Forecasting Using ARIMA Model
The use of currency exchange estimation as a tool for economic planning is being researched as a technique for gaining economic stability. The main purpose of this study is to use the ARIMA model to forecast monthly US dollar and IQ dinar exchange rates. The information was gathered from January 2010 to December 2020. We got the information from the website (sa.investing.com). The minimum value of Root Mean Square Error (RMSE) and the mean absolute error are used to select the optimal model (MAE). ARIMA was found to be the best model for the US Dollar/IQ Dinar series (2, 1,0). This is the forecasted meaning for the future of this exchange rate time series, which indicates a perpetual increase continuously in the next two years. Statgraphics version 15 was the statistical software package utilized to complete this project.
J. E. Buet, G. M. Barber and D. L. Rigby. Elementary Statistics for Geographers. 3rd ed. New York: The Guilford Press, 2009.
W. A. Fuller. Introduction to Statistical Time Series. 2nd ed. Canada: John Wiley and Sons, Inc., 1996.
A. K. Farhan and M. R. Fakhir. Forecasting the exchange rates of the Iraqi Dinar against the US Dollar using the time series model (ARIMA). International Journal of Engineering and Management Research, vol. 9, pp. 2394-6962, 2019.
E. P. Clement. Time series approach to forecasting rate analysis between Nigeria Naira and the U.S dollar. International Journal of Statistics and Applications Statistics, vol. 4, no. 1, pp. 76-84, 2014.
D. H. Kadir. Time series modeling to forecast on consuming electricity: A case study analysis of electrical consumption in Erbil city from 2014 to 2018. Journal of Al Rafidain University College, vol. 46, pp. 473-485, 2020.
G. E. P. Box and G. M. Jenkins. Time Series Analysis Forecasting and Control. 4th ed. USA: Library of Congress Catalog Card Number. 76-8713, 1976.
P. J. Brockwell and R. A. Davis. Time Series: Theory and Methods. 2nd ed. USA, New York: Springer Science and Business Media, LLC, 233 Spring Street, 2006.
H. Lutkepohul and M. Kratzig. Applied Time Series Econometrics. New York: The United States of America by Cambridge University Press; 2004.
J. Cryer and K. Chan. Time Series Analysis with Applications. 2nd ed. USA, New York: Springer Science and Business Media, LLC, 233 Spring Street, 10013, 2008.
P. J. Brockwell and R. A. Davis. Introduction to Time Series and Forecasting. 2nd ed. New York: Springer-Verlag, Inc.; 2002.
R. S. Shumway and D. S. Stoffer. Time Series Analysis and its Applications with R Example. 3rd ed. USA, New York: Springer Science and Business Media, LLC, 233 Spring Street, 2011.
D. C. Montgomery, C. L. Jennings and M. Kulahci. Introduction to Time Series Analysis and Forecasting. 2nd ed. Canada, Hoboken, New Jersey: John Wiley and Sons. Inc., 2016.
W. Wei. Time Series Analysis: Univariate and Multivariate Methods. 2nd ed. USA: Greg Tobin, 2006.
A. Omer, H. Blbas and D. Kadir D. A comparison between brown’s and holt’s double exponential smoothing for forecasting applied generation electrical energies in Kurdistan Region. Cihan University Erbil Scientific Journal, vol. 5, no. 2, pp. 56-63, 2021.
T. Cipra. Time Series in Economics and Finance. Nature, Switzerland, AG, 2020.
G. E. P. Box, G. M. Jenkins and G. C. Reincel. Time Series Analysis Forecasting and Control. 3rd ed. Hoboken, New Jersey: Prentice-Hall, Inc., 1994.
R. A. Yaffee and M. McGee. Introduction to Time Series Analysis and Forecasting with Applications of SAS and SPSS. Tokyo, Toronto: Academic Press, Inc.; 1996.
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