Paper Infomation
Optimization and Empirical Analysis Based on Markov Forecasting Model
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Author: Xinping Yang, Wei Zheng, Yunyuan Yang, Yunyuan Yang
Abstract: The optimization model based on Markov chain is established to optimize the prediction of industrial structure and provide reference for policy adjustment. The vectorization operator is used to transform the Markov prediction model into an optimization problem with constraints, which highlights the theoretical proof and computational rigor. Based on the data of three industrial structures in Yunnan Province from 1989 to 2019, this paper establishes Markov optimization model to predict the proportion of three industrial structures in Yunnan Province from 2020 to 2030. The maximum percentage average absolute error and hill inequality coefficient of the prediction are 1.2335% and 0.2, respectively. The order-degree of the three industrial structures is a stable series, which is stable around 1 after 1996. The sample data and the predicted values show four stages of change characteristics. After 2020, the three industrial structures are stable in the "three, two and one" structure.
Keywords: Markov Chain, Constrained Optimization, Three Industrial Structures in Yunnan Province, Degree of Order, Prediction
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