A novel hybrid method for oil price forecasting with ensemble thought

As a globally hard currency, crude oil has influenced all aspects of the world, especially because volatility in its price seriously affect economies, politics and wars. The prediction of oil prices is challenging due to remarkable price volatility, uncertainty, elusiveness, and complexity. There have been many papers adopting traditional machine learning (ML), economic approaches or combinations of the two; however, these papers have rarely focused on the performance of individual models and ignored less popular but complex models. Considering the above difficulties and situations, in this paper, we design an advanced approach for valuable and robust forecasting of crude oil prices to investigate the differences among the three other popular methods. For this purpose, the Random Forest (RF), XGBoost (XGB), and LightGBM (LGBM) are employed in the proposed hybrid method. The merits of this hybridization lie in the fact that the ensemble model is capable of handling volatile features such as nonlinearity, noncyclicity, and market interrelationships existing in oil price time series; incorporating the strengths of the three single models; not easily overfitting; and finally achieving a better performance. The results reveal that the hybrid method achieves the highest Directional Accuracy (DA) and the lowest errors (0.9612, 13.7417, and 0.0368 of DA, MAE, and MAPE, respectively) compared with the results of three other methods-the RF (0.9569, 14.2699, and 0.0385, respectively), XGB (0.9526, 14.5111, and 0.0387, respectively), and the LGBM (0.9526, 14.4022, and 0.0384, respectively)-in experiments on real datasets.