Multistep Brent oil price forecasting with a multi-aspect aeta-heuristic optimization and ensemble deep learning model
Year:
2024
Type of item:
Articolo in Rivista
Tipologia ANVUR:
Articolo su rivista
Language:
Inglese
Format:
Elettronico
Referee:
Sì
Name of journal:
ENERGY INFORMATICS
ISSN of journal:
2520-8942
N° Volume:
7
Number or Folder:
1
Page numbers:
1-19
Keyword:
Crude oil price forecasting, Brent oil analysis, Time series forecasting, Ensemble learning, Grey Wolf Optimizer
Short description of contents:
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 optimisation 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.
Alruqimi, Mohammed; Di Persio, Luca,
Multistep Brent oil price forecasting with a multi-aspect aeta-heuristic optimization and ensemble deep learning model«ENERGY INFORMATICS»
, vol. 7
, n. 1
, 2024
, pp. 1-19