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A blending ensemble learning model for crude oil price forecasting

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

Annals of Operations Research

Swansea University Author: Mohammad Abedin

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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
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spelling v2 65513 2024-01-26 A blending ensemble learning model for crude oil price forecasting 4ed8c020eae0c9bec4f5d9495d86d415 Mohammad Abedin Mohammad Abedin true false 2024-01-26 CBAE 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. Journal Article Annals of Operations Research 0 Springer Science and Business Media LLC 0254-5330 1572-9338 Forecasting; Crude oil price; Brent; WTI; Blending; Ensemble learning; Stacking regression 25 1 2024 2024-01-25 10.1007/s10479-023-05810-8 COLLEGE NANME Management School COLLEGE CODE CBAE Swansea University SU Library paid the OA fee (TA Institutional Deal) This article was supported by the COST Action Grant CA19130. 2024-05-31T15:06:48.9243732 2024-01-26T11:45:09.4951260 Faculty of Humanities and Social Sciences School of Management - Accounting and Finance Mahmudul Hasan 1 Mohammad Abedin 2 Petr Hajek 3 Kristof Coussement 4 Md. Nahid Sultan 5 Brian Lucey 6 65513__29737__2b875e60547245e1bfcbdc65378d6445.pdf 65513_VoR.pdf 2024-03-18T14:45:14.7284153 Output 2607774 application/pdf Version of Record true This article is licensed under a Creative Commons Attribution 4.0 International License true eng http://creativecommons.org/licenses/by/4.0/
title A blending ensemble learning model for crude oil price forecasting
spellingShingle A blending ensemble learning model for crude oil price forecasting
Mohammad Abedin
title_short A blending ensemble learning model for crude oil price forecasting
title_full A blending ensemble learning model for crude oil price forecasting
title_fullStr A blending ensemble learning model for crude oil price forecasting
title_full_unstemmed A blending ensemble learning model for crude oil price forecasting
title_sort A blending ensemble learning model for crude oil price forecasting
author_id_str_mv 4ed8c020eae0c9bec4f5d9495d86d415
author_id_fullname_str_mv 4ed8c020eae0c9bec4f5d9495d86d415_***_Mohammad Abedin
author Mohammad Abedin
author2 Mahmudul Hasan
Mohammad Abedin
Petr Hajek
Kristof Coussement
Md. Nahid Sultan
Brian Lucey
format Journal article
container_title Annals of Operations Research
container_volume 0
publishDate 2024
institution Swansea University
issn 0254-5330
1572-9338
doi_str_mv 10.1007/s10479-023-05810-8
publisher Springer Science and Business Media LLC
college_str Faculty of Humanities and Social Sciences
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hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Accounting and Finance{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Accounting and Finance
document_store_str 1
active_str 0
description 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.
published_date 2024-01-25T15:06:47Z
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score 11.013216

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