Efficient Use of Markov Chains in Time Chains Forecasting (Population Forecasting Study in the Syrian Arab Republic)

In this research, the Markov chain method was used to predict the population of Syria in terms of educational status. As we mentioned, population statistics are conducted every ten years, so only the sample of data obtained from 1980 to 2010 was studied. It was forecast for three years, 2008, 2009 and 2010, where the forecasting process went through the following steps. In the first stage, the model was designed based on the measure of the mean absolute relative error (MAPE) and the measure of the root mean square error (RMSE). In the second stage, the chart was formed, and in the third stage, the transition matrix from one year to another was formed, using the MATLAB program, and in the final stage, the values were predicted and then the values obtained were compared with the actual values. The obtained values confirm that Markov chains have high reliability and are a reliable method in this study. The research aimed to study all aspects of demographics according to educational stage, build an effective Markov chart model, and use Markov chains in prediction and explain their importance. However, this study produced a set of results and recommendations, the most important of which is that the Markov chain technique is considered one of the important techniques that can be used to predict the future in economic and social studies. There is a high degree of credibility when using Markov chains and their prediction in studying time series of the Syrian population by educational level. The study showed the importance of this technique in economic and social studies due to its reliability and credibility.