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Seasonal time-series modeling and forecasting of monthly mean temperature for decision making in the Kurdistan Region of Iraq

Chawsheen, T.A. and Broom, M. (2017). Seasonal time-series modeling and forecasting of monthly mean temperature for decision making in the Kurdistan Region of Iraq. Journal of Statistical Theory and Practice, doi: 10.1080/15598608.2017.1292484

Abstract

A generalized structural time-series modeling framework was used to analyze the monthly records of mean temperature, one of the most important environmental parameters, using classical stochastic processes. In this article we are using the SARIMA Box–Jenkins model and obtain a medium-term (10 years) forecast of the mean temperature in Erbil. A prediction of the monthly mean temperature during the past 287 months ((Formula presented.)24 years) using the SARIMA(0,1,2)(0,1,1)12 model predicts that the average temperature in the governorate of Erbil, Iraq, will be stable for the next 10 years. The evaluation of prediction accuracy shows that our model performs equally well when applying it to different periods of time for which data is available. The method used here could easily be applied by the decision makers responsible for providing water and electricity in the Kurdistan Region.

Publication Type: Article
Additional Information: This is an Accepted Manuscript of an article published online by Taylor & Francis in Journal of Statistical Theory and Practice on 15/02/2017, available online: http://www.tandfonline.com/10.1080/15598608.2017.1292484.
Publisher Keywords: Climate change, forecasting, Fourier method, Kurdistan Region of Iraq, SARIMA model, stochastic process
Subjects: H Social Sciences > HA Statistics
Q Science > QA Mathematics
Departments: School of Mathematics, Computer Science & Engineering > Mathematics
URI: http://openaccess.city.ac.uk/id/eprint/17181
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