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A blending ensemble learning model for crude oil price forecasting

Mahmudul Hasan, Mohammad Abedin Orcid Logo, Petr Hajek, Kristof Coussement, Md. Nahid Sultan, Brian Lucey

Annals of Operations Research

Swansea University Author: Mohammad Abedin Orcid Logo

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DOI (Published version): 10.1007/s10479-023-05810-8

Abstract

To efficiently capture diverse fluctuation profiles in forecasting crude oil prices, we here propose to combine heterogenous predictors for forecasting the prices of crude oil. Specifically, a forecasting model is developed using blended ensemble learning that combines various machine learning metho...

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Published in: Annals of Operations Research
ISSN: 0254-5330 1572-9338
Published: Springer Science and Business Media LLC 2024
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URI: https://cronfa.swan.ac.uk/Record/cronfa65513
Abstract: To efficiently capture diverse fluctuation profiles in forecasting crude oil prices, we here propose to combine heterogenous predictors for forecasting the prices of crude oil. Specifically, a forecasting model is developed using blended ensemble learning that combines various machine learning methods, including k-nearest neighbor regression, regression trees, linear regression, ridge regression, and support vector regression. Data for Brent and WTI crude oil prices at various time series frequencies are used to validate the proposed blending ensemble learning approach. To show the validity of the proposed model, its performance is further benchmarked against existing individual and ensemble learning methods used for predicting crude oil price, such as lasso regression, bagging lasso regression, boosting, random forest, and support vector regression. We demonstrate that our proposed blending-based model dominates the existing forecasting models in terms of forecasting errors for both short- and medium-term horizons.
Keywords: Forecasting; Crude oil price; Brent; WTI; Blending; Ensemble learning; Stacking regression
College: Faculty of Humanities and Social Sciences
Funders: This article was supported by the COST Action Grant CA19130.

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