  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. In
this paper, we introduce an ensemble model to capture Brent oil price
volatility and enhance the multi-step prediction. Our methodology employs a
two-pronged approach. First, we assess popular deep-learning models and the
impact of various external factors on forecasting accuracy. Then, we introduce
an ensemble multi-step forecasting model for Brent oil prices. Our approach
generates accurate forecasts by employing ensemble techniques across multiple
forecasting scenarios using three BI-GRU networks.Extensive experiments were
conducted on a dataset encompassing the COVID-19 pandemic period, which had a
significant impact on energy markets. The proposed model's performance was
evaluated using the standard evaluation metrics of MAE, MSE, and RMSE. The
results demonstrate that the proposed model outperforms benchmark and
established models.
