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Abstract Accurate crude oil price forecasting is crucial for economic stability, investment planning, and strategic decision-making across various industries. Despite numerous research efforts in applying deep learning to time-series forecasting, achieving high accuracy in multi-step predictions for volatile time-series like crude oil prices remains a significant challenge. Moreover, most existing approaches primarily focus on one-step forecasting, and the performance often varies depending on the dataset and specific case study. This paper introduces ensemble-based deep-learning models to capture Brent oil price volatility and enhance the multi-step price prediction. Our methodology employs a two-pronged approach. First, we present an empirical comparison of deep-learning models and architectures, including RNNs, CNNs, and transformers, for forecasting Brent oil prices. We also examine the impact of various external factors on forecasting accuracy. Then, we introduce a novel approach that employs ensemble GRU-based models to enhance prediction accuracy across multiple forecasting scenarios. Extensive experiments were conducted using a dataset of historical Brent prices encompassing the COVID-19 pandemic, which significantly impacted energy markets. The results demonstrate that the proposed model outperforms benchmark and established models, achieving a 9.3% reduction in MSE compared to the closest benchmark model for a 3-day forecasting horizon. |
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