Enhancing Multi-Step Brent Oil Price Forecasting with Ensemble Multi-Scenario Bi-GRU Networks

TL;DR

This paper proposes an ensemble multi-scenario Bi-GRU network to improve multi-step Brent oil price forecasting accuracy, especially during volatile periods like the COVID-19 pandemic, outperforming existing models.

Contribution

It introduces a novel ensemble multi-scenario Bi-GRU approach for multi-step oil price forecasting, addressing volatility and dataset variability challenges.

Findings

The ensemble model outperforms benchmark models in MAE, MSE, RMSE.

The approach effectively captures oil price volatility during COVID-19.

External factors influence forecasting accuracy significantly.

Abstract

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.