A crude oil price forecasting framework based on Constraint Guarantee and Pareto Fronts Shrinking Strategy

Abstract

Accurate forecasting of crude oil prices is essential for making informed energy policy decisions and ensuring energy security. However, crude oil price forecasting is inherently challenging due to the volatile, nonlinear, and complex nature of the market. While ensemble learning approaches have shown promise in enhancing forecasting accuracy, many existing models rely on multi-objective optimization techniques that generate a Pareto frontier of optimal solutions, often making it difficult to select the best solution for practical application. This issue is exacerbated by the fact that some Pareto-optimal solutions are not suitable for real-world decision-making, leading to inefficiencies in model performance. To address these limitations, this research proposes a novel ensemble learning framework that incorporates a Constraint Guarantee Strategy (CGS) and a Pareto Front Shrinking Strategy (PFSS) to enhance both the accuracy and stability of crude oil price forecasting models. The CGS filters out inferior solutions during the optimization process, ensuring that the ensemble model outperforms individual models in terms of forecasting accuracy. The PFSS helps decision-makers select the most relevant solutions from the Pareto frontier by balancing trade-offs between objectives and narrowing down the set of solutions. Our framework is evaluated on three widely used datasets: Brent, WTI, and Dubai crude oil prices, and compared with state-of-the-art models from both the general time-series forecasting domain and crude oil price forecasting. It improves prediction accuracy by approximately 23.2% on the Brent dataset, 4.0% on the WTI dataset, and 21.7% on the Dubai dataset, based on improvements in MAPE. Ablation studies confirm the effectiveness of each component. The discussion further emphasizes the practical applicability and robustness of the framework, confirming its potential for real-world crude oil price forecasting.