Oil Price Forecasting Model Based on GARCH-LSTM Model

Abstract

 Based on the importance of oil in daily life and economic development, the price prediction of oil has become a pressing problem. Previously, the methods for oil price prediction were broadly divided into two categories, respectively, statistical-based prediction, which has more reliable mathematical principles, but the prediction effect is not very ideal, and deep learning methods, which use neural networks for prediction, although the prediction effect is more ideal, but there are still many mathematical features in the data have not been completely extracted. Therefore, this paper combines the statistical method with the deep learning method, the statistical method selects the GARCH family model, and the deep learning method selects the LSTM model, and establishes the GARCH-LSTM hybrid model for oil price prediction. The experimental data are selected from Brent crude oil from January 2000 to December 2022. After fitting the data using different models, by calculating their corresponding fitting accuracy, it can be found that the fitting accuracy of the GARCH-LSTM model is significantly better than other models, and its RMSE corresponds to 0.148% and MAE to 1.378%. The model can be trained both by the mathematical features in the data through the GARCH model and in depth using the LSTM neural network to obtain more excellent training results.