Empirical Analysis And Forecast Of Yili Prefecture GDP Based On Improved Grey Markov Chain

GPD is an important comprehensive indicator of national economic accounting,and its changes can reflect changes in the economic development of a country or region.With the implementation of the "The Belt and Road" strategy,the unique geographical advantages of Yili Prefecture gradually become prominent in the core area of the economic belt The important strategic position has accelerated the pace of China's westward economic development.In recent years,the economic level of Yili Prefecture has been continuously improved.In 2019,Yili Prefecture's GPD ranked fourth in Xinjiang.Compared with 2018,the ranking has risen,and the nominal GPD has increased.The quantity is also in the forefront,indicating that the economy of Yili Prefecture has developed rapidly throughout the year,and the overall performance of various industries is better,which promotes the economy to be better than other cities.Therefore,scientific research on the GPD of Yili Prefecture is of far-reaching significance for future economic development.In this paper,based on the traditional GM(1,1)model,a GM(1,1)model with optimized background value,a gray Markov chain model,and a gray Markov chain model modified by particle swarm optimization are established respectively,and Yili is selected.State GPD from 2011 to 2019 conducts empirical analysis.By optimizing the background value of the traditional GM(1,1)model,it reduces the parameter error generated by using the adjacent mean to define the background value during the prediction process,and improves the prediction accuracy of the model.Then use The organic fusion of Markov chain and GM(1,1)model after optimizing the background value effectively overcomes the limitations of gray model processing data fluctuations.This combined model not only reflects the overall forecasting trend of GPD in Yili Prefecture,but also makes up for The deficiencies of the GM(1,1)model.Finally,the particle swarm algorithm is used to modify the combined model twice,which reduces the error generated by the gray Markov chain model using the gray interval median as the predicted value.The results show that the model has a better performance.Good fit and prediction accuracy.Therefore,the modified model of particle swarm algorithm is used to predict the GPD of Yili Prefecture from 2020 to 2022.The prediction results can provide effective suggestions for the government and relevant departments on the future economic development of Yili Prefecture,and it has certain reference and reference significance for the deployment and adjustment of future economic policies.