  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
combining metaheuristic optimisation and an ensemble of five popular neural
network architectures used in 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.
