A novel forecasting model for crude oil prices that integrates CEEMDAN-VMD multiscale decomposition with an Attention-based Bidirectional LSTM network

Accurate prediction of WTI crude oil prices is of great significance for the business decision-making of oil and gas enterprises, the formulation of national energy strategies, and the risk management of the global financial market. However, current traditional prediction methods have some limitations in predicting WTI crude oil prices. For instance, traditional methods are insufficient in integrating complex factors that affect international oil prices, such as geopolitical conflicts, global supply and demand imbalances, and financial speculation. They also have limited ability to extract nonlinear and time-varying correlation features between oil prices and multiple influencing factors, and do not adequately consider data noise. As a result, it is difficult to clearly explain the contribution mechanism of key influencing factors to the prediction results of oil prices, and the model's interpretability is insufficient. To resolve these challenges, this paper integrates Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Variational Mode Decomposition (VMD), Attention Mechanism (Attention), and Bidirectional Long Short-Term Memory (BiLSTM), and proposes a deep learning-based hybrid prediction model (CEEMDAN-VMD-Attention-BiLSTM). Specifically, the non-stationary and non-linear characteristics of the WTI oil price series are decomposed into several stationary sub-series by using CEEMDAN-VMD, reducing data noise and capturing the non-linear relationship between crude oil prices and macroeconomic variables. An Attention-BiLSTM model is constructed to predict the sub-series of oil prices decomposed by CEEMDAN-VMD, and the predicted values of these sub-series are summed to reconstruct the final predicted value. In order to augment the interpretability of the model's forecast results, the SHAP method is adopted to quantify the contribution of different input parameters to the model's prediction results. Based on 28 years of time series data, the study shows that the MAPE of the proposed hybrid prediction model is 7.66%, and the R² is 0.9665. The proposed model demonstrates superior predictive accuracy and notably robust performance in comparative analysis. Through SHAP analysis, the top 5 key factors influencing international oil prices are Brent Crude Oil Price, LBMA Gold Price, Federal Funds Effective Rate, RMB-USD Exchange Rate, and Henry Hub Natural Gas Spot Price. The proposed model helps countries grasp the trend of the crude oil market and provides scientific basis for the formulation of energy policies.