A blending ensemble learning model for crude oil price forecasting

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

A blending ensemble learning model for crude oil price forecasting

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

A blending ensemble learning model for crude oil price forecasting

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

50206 - Finance

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

Annals of Operations Research

ISSN

0254-5330

e-ISSN

1572-9338

Svazek periodika

353

Číslo periodika v rámci svazku

Neuveden

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

31

Strana od-do

485-515

Kód UT WoS článku

001148036800001

EID výsledku v databázi Scopus

2-s2.0-85182995082