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Research on Time Series Problem Model Based on Dynamic Network NAR and Multiple Regression

With the escalation of the trade war between China and the United States and the outbreak of the new coronavirus epidemic in early 2020, China’s economy has been seriously affected. Accurate prediction of future economic development is a necessary means to formulate economic development strategies. For this reason, taking Guangdong province, the province with the strongest economic vitality, as an example, this paper uses multiple regression method to calculate the correlation degree and influence weight of population number, number of enterprises and per capita disposable income with economic development, and obtains multiple regression linear equation. In addition, this paper also uses the NAR dynamic neural network model in machine learning algorithms to predict the future trends of the three factors and economic aggregates, and analyzes the feedback results of network training errors, autocorrelation values, and partial correlation values. Compared with the multiple regression method, it is found that the final results of the two are very similar, with small errors and high correlation.