Some commonly used methods, such as multiple regression and time series method, are typical linear forecasting models, which can predict the linear relationship of price changes and have certain limitations on medium and long-term forecasting.

Neural network is an intelligent forecasting method, which needs to establish an accurate mathematical model of the object, can accurately describe the characteristics of the object, and has the characteristics of strong learning ability and parallel processing. It has been widely used in complex system modeling and other fields, and also provides ideas for oil price forecasting. However, because there are many factors that affect oil prices, and the data contains noise, the direct use of neural network learning has slow convergence speed and low prediction accuracy, so it is necessary to preprocess the factors that affect oil prices. According to the characteristics of oil price changes, a prediction model of oil price based on principal component analysis and BP neural network is proposed. The model uses qualitative analysis to screen the influencing factors, then uses principal component analysis to screen the influencing factors of oil price, and selects the most important influencing factors. Finally, the oil price is predicted by the ability of neural network to approximate the nonlinear continuous function, so as to realize the accurate prediction of oil price.

Price forecasting process

The prediction process of BP neural network is as follows:

1. Collect oil price and its influencing factors, and obtain relevant data.

2. Make principal component analysis on the influencing factors of oil price, and select the principal component with large contribution rate.

3. Divide the oil price data into training samples and forecasting samples.

4. BP neural network is used to train the training samples and find out the optimal parameters of the model.

5. Use the most parameters to predict the prediction samples, and establish a BP neural network prediction model.