Forecasting for the Imported Weight of Equipment to Cargo of Sulaimani International Airport

published

DOI http://dx.doi.org/10.25098/3.1.5

Issue

Vol 3- Issue 1 JUN 2019

 Handreen Abdulla Tahir¹, Renas Abubaker Ahmed², Aras Jalal Mhamad^3,4

¹Department of Statistics and computer, College of Commerce, University of Sulaimani, Sulaymaniyah City, Iraq.

²Department of Statistic and Informatics, College of Administration and Economics-University of Sulaimani, Sulaymaniyah City, Iraq.

^3,4Accounting Department, College of Administration and Economics, University of Human Development, Sulaymaniyah City, Iraq.

[email protected]^3,4

 

---

 

Received : 21-4-2019                                Revised:25-5-2019

Accepted : 6-6-2019                                  Published :30-6-2019

 

---

Abstract

The problem of a weight of imported equipment in the airport with their effects on economic situation is one of the most important problems that challenges faced in the airports of the region especially international Sulaymaniyah airport. This study aims to analyze the time series of weight of imported equipment in international Sulaymaniyah airport for the period between (Jan; 2010 to Nov; 2017) using the modern style in analyzing the time series which is (Box-Jenkins) method for the accuracy and flexibility it has in addition to its high efficiency in analyzing the time series. In this study, we are interested in forecasting the weight of imported equipment of international Sulaymaniyah airport using Box- Jenkins method. The study found that the fit and efficient model is shown according to smallest measurements (AIC, RMSE, MAPE and MAE) is the seasonal model of lag 4 (SARIMA (1,1,1)x(2,0,0)4). According to the results of SARIMA (1,1,1)×(2,0,0)4, the amounts of the weight of imported equipment of international Sulaymaniyah airport have been forecasted for the period from Nov; 2017 to Oct; 2018 (the forecasting was done for 12 months).

Finally, we recommend decision makers and the interested people to adapt to formulate strategic plan depend mainly on the scientific method in forecasting of the monthly weight of imported equipment since there is a real problem facing international Sulaymaniyah airport throughout the upcoming years.

 

Keywords: Time Series Analysis, SARIMA model, Forecasting.

 

پوخته‌:

كێشه‌ى هاورده‌كردنى كه‌لوپه‌ل له‌ڕێگه‌ى فڕۆكه‌خانه‌ى نێوده‌وڵه‌تى و كاریگه‌رى له‌سه‌ر بارى ئابوورى یه‌كێكه‌ له‌و كێشانه‌ى كه‌ فڕۆكه‌خانه‌كانى ناوچه‌كه‌ ڕووبه‌ڕوى ده‌بنه‌وه‌ به‌تایبه‌تى فڕۆكه‌خانه‌ى نێوده‌وڵه‌تى سلێمانى, ئامانجى ئه‌م توێژینه‌وه‌یه‌ شیكاریكردنى بڕى كێشى كه‌ل وپه‌لى هاوورده‌یه‌ له‌ڕێگه‌ى فڕۆكه‌خانه‌ى نێوده‌وڵه‌تى سلێمانى به‌به‌كارهێنانى تیۆرى (بۆكس-جنكس), هه‌ڵبژاردنى ئه‌م تیۆریه‌ بۆ شیكاریكردن و خه‌مڵاندنى باشترین چه‌ماوه‌ ده‌گه‌ڕێته‌وه‌ بۆ وردى و چوستى و بێخه‌وشى تیۆریه‌كه‌, له‌م توێژینه‌وه‌یه‌دا توێژه‌ران گرینگیانداوه‌ به‌ پێشبینى كردنى كێشى كه‌ل و په‌لى هاوورده‌كراو بۆ شارى سلێمانى له‌ ڕێگه‌ى فڕۆكه‌خانه‌ى نێوده‌وڵه‌تى سلێمانى, له‌دواى شیكاریكردن باشترین چه‌ماوه‌ دیاریكرا كه‌ بریتیه‌ له‌   SARIMA (1, 1, 1) به‌ پشتبه‌ستن به‌چه‌ند پێوه‌رێكى به‌راووردى ئامارى وه‌ك (AIC, MAE, RMSE, MSE) له‌سه‌ر بنه‌ماى چه‌ماوه‌ى دیاریكراو پێشبینى كرا بۆ دوانزه‌ مانگى داهاتوو كه‌له‌ (سه‌رما وه‌رزى 2017 تا گه‌ڵا ڕێزانى 2018). له‌كۆتایى دا داوا له‌ لایه‌نه‌ به‌رپرسه‌كان ده‌كه‌ین كه‌ هاورده‌كردنى كه‌ل و په‌ل به‌پێى پێویستى بێت له‌ چوارچێوه‌ى ستانداردى جیهانی وه‌ هه‌روه‌ها ئاماده‌كارى بكرێت بۆ ئه‌و نه‌خوازراوانه‌ى كه‌ ڕووبه‌ڕوى فڕۆكه‌خانه‌ى نێوده‌ڵه‌تى سلێمانى ده‌بنه‌وه‌.

الملخص

        تعد مشكلة ثقل المعدات المستوردة عبر المطار وتأثيرها على الوضع الاقتصادي من أهم المشكلات التي تواجهها المطارات وخاصة مطار السليمانية الدولي. تهدف هذه الدراسة إلى تحليل السلاسل الزمنية لوزن المعدات المستوردة في مطار السليمانية الدولي للفترة من (كانون الثاني ؛ 2010 إلى تشرين الثاني؛ 2017) باستخدام الأسلوب الحديث في تحليل السلسلة الزمنية و التي هي (بوكس-جنكس) لأن هذه الطريقة لديها    دقة و كفائه العالية في تحليل السلاسل الزمنية. في هذه الدراسة، نحن مهتمون بالتنبؤ بوزن المعدات المستوردة من مطار السليمانية الدولي باستخدام طريقة (بوكس-جنكس)، بعد تحليل الأحصائي في هذه الدراسة تم تحديد النموذج الملائم  بالأعتماد على المقايس المقارنة الأحصائية AIC) و RMSE و MAPE و  (MAE وهي النموذج الموسمي  ذي إزاحة الزمنية 4. وفقًا لنتائج SARIMA(1,1,1)×(2,0,0)4،  فقد تم التنبؤ بمقدار أوزان المعدات المستوردة  من مطار السليمانية الدولي للفترة  الزمنية تشرين الثاني ؛ 2017 إلى تشرين الأول ؛ 2018. أخيرًا ، نوصي جهات المعنية والأشخاص المهتمين بالتكيف لصياغة الخطة الإستراتيجية التي تعتمد بشكل أساسي على الطريقة العلمية في التنبؤ بالوزن الشهري للمعدات المستوردة، وانتباه  للمشاكل الحقيقية  التي قد تواجه المطار السليمانية الدولي خلال السنوات المقبلة.

References

1. Adhistya Erna Permanasari, Indriana Hidayah and Isna Alfi Bustoni (2013), “SARIMA (Seasonal ARIMA) implementation on time series to forecast the number of Malaria incidence” Conference Paper· October 2013 with 1,996 Reads, Conference: Conference: Information Technology and Electrical Engineering (ICITEE), 2013 International Conference.
2. Agyemang, Boakye. (2012), Autoregressive Integrated Moving Average (ARIMA) Intervention Analysis Model for the Major Crimes in Ghana. (The case of the Eastern Region), BSc, Department of Mathematics, Kwame Nkrumah University of Science and Technology, Ghana.
3. A new look at the statistical model identi_cation. IEEE Transactions on Automatic Control, 19(6):716{723, 1974.
4. Box, G. E. P., Jenkins, G. M., (1970), Time Series Analysis, Forecasting and Control, 3rd ed. Prentice Hall, Englewood Cliffs, New Jersey.
5. Biljana Petrevska and Goce Delcev (2016), “Predicting tourism demand by A.R.I.M.A. models”, Economic Research-Ekonomska Istraživanja, ISSN: 1331-677X (Print) 1848-9664 (Online) Journal homepage: http://www.tandfonline.com/loi/rero20, 2017
6. Chatfield, C. (1996), The Analysis of Time Series, 5th ed., Chapman & Hall, New York.
7. Charles, Akinmutmi. (1998-2011) , A seasonal Arima Modelling of Residential Coal Consumption Series in Nigeria, Department of Mathematics, Ahmadu Bello University, Nigeria.
8. Cochrane, J. H. (2005). Time Series for Macroeconomics and Finance. Chicago: Graduate School of Business, University of Chicago.
9. Cryer, J. D, Chan, K.(2008), Time Series Analysis, Second Edition, Springer Science Business Media, New York.
10. Elangbam Haridev Singh, 2013, “Forecasting Tourist Inflow in Bhutan using Seasonal ARIMA”, International Journal of Science and Research (IJSR), India Online ISSN: 2319-7064 , Volume 2 Issue 9, September 2013.
11. Daniel Eni and Fola J. Adeyeye (2015), “Seasonal ARIMA Modeling and Forecasting of Rainfall in Warri Town, Nigeria”, Journal of Geoscience and Environment Protection, 2015, 3, 91-98 Published Online August 2015 in SciRes.
12. (2010). Time Series Analysis. Retrieved March 2015, from DTREG: https://www.dtreg.com/methodology.
13. Ette H. Etuk (21 Aug 2014), “An additive seasonal Box-Jenkins model for Nigerian monthly savings deposit rates”, Conference Paper on (18-Apr 2014).
14. Gyasi-agyei , Kwame. (june,2012), Analysis and modeling of prevalence of measles in the ashnti region of chana, Kwame Nkrumah University of Science and Technology, West Africa.
15. Hamilton, J. D.( (1994), Time series analysis, Princeton university press United State of America.
16. Hudu, Muhammed. (2009), Interrupted Time Series Analysis of the Rate of Inflation in Ghana, MSc, Department of Mathematics, Kwame Nkrumah University of Science and Technology, West Africa.
17. Hurvich, C.M., and Tsai, C.L (1989). “Regression and Time Series Model Selection in Small Sample”, Biometrika, 76, 297-307.
18. John A. D. aston, David F. Findley, Tucker S. Mcelroy , Kellie C. Wills and Donald E. K. Martin (2007), “New ARIMA Models for Seasonal Time Series and Their Application to Seasonal Adjustment and Forecasting”, Conference Paper on (18-oct 2007).
19. Kibar, Mustafa alptekin. (2007), Building Cost Index Forcasting with Time Series Analysis, MSc, Civil Engineering Department, Middle East Technical University, Turkey.
20. Mynsbrugge, Jorrit V. (2010), “Bidding Strategies Using Price Based Unit Commitment in a Deregulated Power Market”, K.U.Leuven, 62, 24-28.
21. Reinsel, G. C. (1994), Time Series Analysis, Forecasting and Control, 3^rd Prentice Hall, Englewood Clifs, New Jersey.
22. Sariaslan , Nazli. (2010) , the Effect of Temporal Aggregation on Univariate Time Series Analysis, department of statics, Meddle East Technical University, Turkey.
23. Shumway, R. Stoffer, H. (2006), Time Series Analysis and Its Applications, Second Edition, Springer Science, Business Media, New York
24. Spyros, M., Wheelwright, S. C. and Hyndman, R. J. (1998), “Forecasting: Methods and Applications”, Third Edition, John Wiley & Sons, Inc, U.S.A.
25. William W. S. (1989). “Time Series Analysis: Univariate and Multivariate Method”. University of Tempie, department of statistic. Addison-Wesley publishing company, Inc.