Abstract
In order to improve the accuracy of forecasting crude oil prices, a new crude oil price forecasting method is introduced in the paper that is a combination of the FNN model and the stochastic time effective function—namely, the WT-FNN model. The FNN model keeps track of the historical values of crude oil prices and predicts future crude oil prices, and the stochastic time effective function gives greater weight to recent information and smaller weight to old information, thus making the prediction of crude oil prices more reasonable. We selected the daily data of Brent crude oil prices from 4 January 2000 to 30 September 2021 as research objects and then used the WT-FNN model to train and predict the research objects. By comparing it to the benchmark model, we found that the predictive effect of the WT-FNN model was better than the FNN model and the no-change model. The results also passed a robustness test.
Highlights
A sharp rise in crude oil prices causes an economic recession in crude oil import countries, and a drop in crude oil prices causes a fiscal deficit in crude oil export countries
The predictive values of WT-feedforward neural network (FNN) and FNN were better fitted with the real value; in the second half of 2014, the second half of 2015 to the first half of 2018, early 2018, and the first half of 2020, crude oil prices fluctuated heavily, and the predictive values of the three models (WT-FNN, FNN, and no-change) varied sharply from the true value
5, the predictive value of the WT-FNN model was closer to the real value than the predictive values of other models; in other words, the WT-FNN model had the best predictive effect, and the FNN model outperformed the common no-change model
Summary
Crude oil is an important industrial raw material and a financial instrument. The fluctuations in crude oil prices have a significant impact on the world’s economy and financial markets [1,2]. The impact of crude oil price fluctuations is manifested in two main ways. Some sectors of the economy and finance depend on the forecasts of crude oil prices for their business. It is of great importance to take a scientific approach to crude oil forecasting to avoid price risks and to grasp investment opportunities. The method of forecasting crude oil prices mainly includes the traditional econometrics method, a machine learning method, and a combined method. Specific research on these three methods is as follows
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