Using random time series to predict barley production in Iraq
Keywords:
Residual, Differences, Model, Inverse roots, DiagnoseAbstract
The use of random time series is one of the important statistical methods to forecast the studied chain and production of barley in Iraq. Barely has economic and livelihood importance. Also, it is used as livestock to feed animal because of the many problems facing farmers such as wars, water scarcity, migration and increasing salts Soil ... etc. This in itself is one of the prominent reasons that prevented the Iraqi farmer from directing attention to the production of barley. Because of its importance, barely is used in this research to predict its production in Iraq. Random time series models were used to obtain the best prediction model.
The results were reached using statistical methods to find out the reality of the development of production during 1965 to 2019. Also, the EVIEWS 10 program was used to reach the results. It was found that the extent of similarity of production between the governorates of Anbar and Salah al-Din according to the variables of sustainable development. The results show that the series does not It suffers from the heterogeneity of the variance nor from the seasonal variations of the studied series. In addition, the appropriate and best selected models for prediction are AR (1), MA (1), ARMA (1,1). Testing and estimating the parameters show that the best model is the AR model ( 1). It helped to obtain the predictive equation for the model and prediction for the years 2020 and 2021. Furthermore, it proved its accuracy and its close predictive value
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