A blending ensemble learning model for crude oil price forecasting

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F25%3A39923434" target="_blank" >RIV/00216275:25410/25:39923434 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/article/10.1007/s10479-023-05810-8" target="_blank" >https://link.springer.com/article/10.1007/s10479-023-05810-8</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10479-023-05810-8" target="_blank" >10.1007/s10479-023-05810-8</a>

Result language

angličtina

Original language name

A blending ensemble learning model for crude oil price forecasting

Original language 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.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

50206 - Finance

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Annals of Operations Research

ISSN

0254-5330

e-ISSN

1572-9338

Volume of the periodical

353

Issue of the periodical within the volume

Neuveden

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

31

Pages from-to

485-515

UT code for WoS article

001148036800001

EID of the result in the Scopus database

2-s2.0-85182995082