Time Series Forecasting Using Arima Methodology with Application on Census Data in Iraq

  • Qais M. Abdulgader Dohuk Polytechnic University
Keywords: Box-Jenkins, ARIMA Models, Time Series Forecasting, Census


In this paper, the methodology of Box-Jenkins of Autoregressive Integrated Moving Average (ARIMA) has been used for applying and forecasting the census in Iraq by taking (61) observations of the annually census from 1950 to 2010. Several adequate models of time series have been built and some of the performance criteria have been used for the purpose of comparison between models. Results of the analysis showed that the ARI(2,2) model is adequate to be used to forecast the annually census data of Iraq. During the period 2011 to 2020, there will be (33.58%) increase in the population, and the population of Iraq in 2020 would be (41358200) persons.

Author Biography

Qais M. Abdulgader, Dohuk Polytechnic University

Dept. of Hospital Management, Zakho Technical Institute, Dohuk Polytechnic University, Zakho, Iraq.


Ayalew S., Babu M. C. and Raw L.K. (2012). Comparison of New Approach Criteria for Estimating the Order of Autoregressive Process. Journal of Mathematics, 1, 10-20.
Box G.E.P. and Cox D.R. (1982). An analysis of transformations, revisited, rebutted. Journal of American Statistical Association, 77, 209-210.
Brajesh and Shekhar C. (2015). Accidental mortality in India: Statistical models for forecasting. International Journal of Humanities and Social Science Invention, 4, 35-45.
Brockwell P. J. and R. A. Davis R. A. (2002). Introduction to time series and forecasting. (2nd ed.). Springer.
Brooks C. and Tsolacos S. (2010). Real Estate Modeling and Forecasting. Cambridge university press.
George E., Jenkins G. M. and Reinsel G. C. (2008). Time series analysis: Forecasting and control, (4th ed.). John Wiley & Sons, INC.
Ghafil A. A. (2013). Using Box-Jenkins (ARIMA) models for forecasting the production of electric power. Journal of Karbala University, 11, 196-207.
Makridakis S., Wheelwright S. C. and R. J. Hyndman R. J. (1998). Forecasting: Methods and applications. (3rd ed.). John Wiley & Sons, INC.
Mutar D. R. and I. I. Ilias I. I. (2010). Analysis and modeling time series of water flow into Mosul city: A comparative study. Iraqi Journal of Statistical Sciences.10, 1-32.
Ngo T. H. D. and Bros W. (2013). The Box-Jenkins Methodology for Time Series Models. Journal of statistics and data analysis, 454, 1-13.
Pang O. and McElroy T. (2014). Forecasting Fertility and Mortality by Race/Ethnicity and Gender. Center for Statistical Research & Methodology, 3, 1-55.
Polhemus N. W. (2011).Time Series Analysis Using Statgraphics Centurion. StatPoint Technologies, INC.
Sarpong S. A. (2013). Modeling and Forecasting Maternal Mortality; an Application of ARIMA Models. International Journal of Applied Science and Technology, 3, 19-28.
Shumway R. H. and Stoffer D. S. (2011). Time series analysis and its applications. (3rd ed.). Springer.
Tsay R. S. (2002). Analysis of Financial Time Series. John Wiley & Sons, INC.
Tuama S. A.(2012). Using analysis of time series to forecast numbers of the patients Malignant Tumors in Anbar province. Al-Anbar University Journal of Economics and Administration Sciences, 4, 371-393.
Wan W-Y, Moffatt S., Xie Z., Corben S. and Weatherburn D. (2013). Forecasting prison populations using sentencing and arrest data. Crime and Justice Bulletin, 174, 1-12.
Yaffee R. and McGee M. (1999). Introduction to time series analysis and forecasting: With applications of SAS and SPSS. Academic Press.
Zakria M. and Muhammad F. (2009). Forecasting the population of Pakistan using ARIMA models. Pakistan Journal of Agricultural Science, 46, 214-223.
How to Cite
Abdulgader, Q. (2016). Time Series Forecasting Using Arima Methodology with Application on Census Data in Iraq. Science Journal of University of Zakho, 4(2), 258-268. Retrieved from https://sjuoz.uoz.edu.krd/index.php/sjuoz/article/view/359
Science Journal of University of Zakho