This study examines the prediction in time series of industrial production, showing how the accuracy of predictions obtained by the statistical models used in the study, and the ability of these models to deal with changes in time series data and the changes on the performance of these models. The study aimed mainly to find the most suitable and best statistical forecasting models to be used in the process of forecasting cement production in Sudan. Using the Box-Jenkins methodology and the exponential smoothing method (Holt model), a number of models were constructed for the study sample of the time series of cement production in Sudan. The results of the study showed that the Box-Jenkins model outperformed the Holt model, where the comparative criteria indicators indicated the preference of the ARIMA model (2,1,1). Standards. The results showed that the results of future predictions obtained using the selected model ARIMA (2,1,1) are all within the limits of confidence, which indicates the quality and accuracy of this model in the prediction. Prediction values have indicated an increase in productivity over the next 10 years. Random changes were the most important factors that directly affected the performance of both models, but they were more effective in reducing the efficiency of the Holt exponential model compared to the Box-Jenkins model.
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