Forecasting Oil Demand with the Development of Comprehensive Tourism

The prediction of oil demand is an important issue related to national energy security and economic development. With the COVID-19 outbreak, the international oil price fluctuates sharply, and oil consumption growth slows down. Therefore, accurate prediction of oil demand plays an important practical and theoretical role. In this paper, in accordance with the Chinese state policy stimulation of domestic demand for energy resources, we have selected 15 major factors and analyzed their influence on the domestic oil demand from the perspective of comprehensive tourism analysis. Based on the data analysis of oil consumption from 2000 to 2018, four neutral network methods are used to predict the influence of selected factors on oil consumption demand of China. The experimental results show that the best correlation is obtained between domestic tourism revenue and total tourism expenditure factors and oil demand, and the Layer Recurrent Neutral Network method has high prediction accuracy, stronger stability, and the best performance.

Automated summary

This paper analyzes the influence of 15 major factors on domestic oil demand in China from the perspective of comprehensive tourism analysis. It is found that domestic tourism revenue and total tourism expenditure show the best correlation with oil demand. The Layer Recurrent Neural Network method demonstrates high prediction accuracy and stability in forecasting the influence of selected factors on oil consumption demand in China.