On June 15th, Yuanhong Liu made an online academic report entitled "Comparative Study on Combination Forecast of China's Crude Oil Production Based on ARIMA and NAR Neural Network Models".Part of the teachers and graduate students attended the lecture presided by Ke Jiang.

First, Yuanhong Liu introduced the current situation of my country's energy production and energy consumption in detail, and pointed out that my country's energy production and energy consumption structure has the characteristics of high proportion of coal and low proportion of oil. Secondly, Yuanhong Liu conducts empirical research on the monthly data of my country's crude oil production from January 1986 to October 2021, and constructs an ARIMA-NAR neural network combined prediction model according to the two characteristics of data non-stationarity and strong volatility. The study found that the ARIMA-NAR neural network combination prediction model based on model combination has higher prediction accuracy and can effectively predict my country's crude oil production, thereby providing a more scientific decision-making basis for my country to formulate energy policies. Finally, Yuanhong Liu puts forward policy suggestions for optimizing my country's energy production and energy consumption structure based on relevant research conclusions.

The academic forum which is informative and insightful broadened the horizons of the students and teachers, providing them with a deeper understanding of the country's energy production and energy consumption structure.

Copywriters: GuanyuXu, Ze Zhang    Verifier: Xiang Li