Multistep Brent Oil Price Forecasting with a Multi-Aspect Meta-heuristic Optimization and Ensemble Deep Learning Model

TL;DR

This paper introduces a hybrid multi-aspect metaheuristic and ensemble deep learning approach for Brent oil price forecasting, significantly enhancing accuracy by optimizing multiple model components and external factors.

Contribution

It presents a novel multi-level GWO-based optimization framework that improves deep learning forecasts by tuning features, data, training, and blending, outperforming existing methods.

Findings

Achieved a low MSE of 0.000127 on Brent crude oil data.

Enhanced forecasting accuracy over traditional models.

Demonstrated robustness across different external conditions.

Abstract

Accurate crude oil price forecasting is crucial for various economic activities, including energy trading, risk management, and investment planning. Although deep learning models have emerged as powerful tools for crude oil price forecasting, achieving accurate forecasts remains challenging. Deep learning models' performance is heavily influenced by hyperparameters tuning, and they are expected to perform differently under various circumstances. Furthermore, price volatility is also sensitive to external factors such as world events. To address these limitations, we propose a hybrid approach that integrates metaheuristic optimization with an ensemble of five widely used neural network architectures for time series forecasting. Unlike existing methods that apply metaheuristics to optimise hyperparameters within the neural network architecture, we exploit the GWO metaheuristic optimiser at four levels: feature selection, data preparation, model training, and forecast blending. The proposed approach has been evaluated for forecasting three-ahead days using real-world Brent crude oil price data, and the obtained results demonstrate that the proposed approach improves the forecasting performance measured using various benchmarks, achieving 0.000127 of MSE.