Introducing the Synergy Based Approach for Forecasting the Crude Oil Prices with Traditional and Machine Learning Econometric Models

Crude oil plays a pivotal role in the global economy, influencing inflation, tradebalances, and energy security. Accurate forecasting of crude oil prices is therefore essential forpolicymakers and market participants. This study proposes a hybrid forecasting framework thatsynergizes conventional econometric methods with machine learning (ML) techniques. . First, thetime series is decomposed using Ensemble Empirical Mode Decomposition (EEMD) to isolateintrinsic mode functions (IMFs). These components are then classified into deterministic andstochastic elements via spectral analysis. Second, traditional models such as ARIMA and GARCHare applied to the relevant IMFs, while advanced ML models (LSTM and XGBoost) are fitted toboth original and residual series. Finally, a synergy model combines econometric and ML outputs,with Bayesian optimization applied for hyperparameter tuning. . Model performance is assessedusing key error metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE),and Mean Absolute Percentage Error (MAPE). The findings suggest that hybrid models integratingconventional econometric methods with machine learning approaches, optimized throughBayesian techniques, achieve superior forecasting accuracy compared to standalone models.Additionally, the Diebold-Mariano (DM) test confirms that these synergy-based models offer themost reliable predictions for crude oil prices.