Forms of Artificial Neural Networks for International Crude Oil Price Forecasting Research Based on ADF and GCT Post-Tests

Junjie Zhao; Guangjie Liu

doi:10.54097/dg8w4d17

Authors

Junjie Zhao
Guangjie Liu

DOI:

https://doi.org/10.54097/dg8w4d17

Keywords:

ADF test, Mechanical learning, Artificial neural networks, Crude oil price forecasting model.

Abstract

Crude oil is the core of all types of energy for industrial production and transportation as well as for daily life, and is an important strategic resource relative to social security, world peace and the maintenance of its international status. With the changes in international crude oil prices and market finance and economy gradually began to closely linked between its price fluctuations not only affect the cost of energy and economic growth, but also on the financial market and international trade has a far-reaching impact. Firstly, the ADF test is used to determine the smoothness of the time series of crude oil prices; secondly, the causal relationship between variables with different time lags is analyzed by Granger causality test; finally, based on the results of the above test, the ANN forecasting model is constructed and optimized to forecast the international crude oil prices in the short term. The ANN forecasting model based on ADF and Granger test proposed in this study provides a new idea and method for international crude oil price forecasting, which has high practical application value.

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