Improved Grey Markov Model To Predict Post And Telecommunications Business

A scientific and reasonable prediction model is beneficial to the research of a phenomenon, andalso has great significance in making correct decisions. This article makes analysis on the data ofnational posts and telecommunications business based on the GreyGM(1,1) model, and then correctthe model taking the metabolism method into use. By combining the corrected model and Markovchain, the article puts forward a new prediction model called Improved Grey Markov Model. Thenew model provides a more satisfactory prediction result, which has a promotion in both precisionand accuracy.This article is divided into five parts as follows:The first section brings out the problem and states the meaning and function of the research. Thebasic knowledge of posts and telecommunications business and several current prediction methodsare introduced in this part.The second section talks about the definition of Markov process, transition probability, theclassification and decomposition of states and the basic knowledge of precision verification. It alsodescribes the Grey GM(1,1) model and the residual GM(1,1) model.The third section explains the principle of Grey Transition Probability Markov model and GreyStates Markov model, and applies them into the prediction of national posts andtelecommunications business to get a preliminary result. The result shows the Grey Markov modelhas certain feasibility.The fourth section mainly introduces two improved methods, one of which is metabolism, andanother is to combine metabolism and residual amendment. The ultimate experimental results showthat these two improved models can both give exact interval of prediction data and the data aremore accurate than the normal Grey Markov model gives. Comparing these two improved models,the latter performs even better than the former.The last section gives the summary of this paper and the prospect of the research field.