A weekly crude oil price interval-valued prediction architecture on fusion of decomposition technique and adaptive integration

Yuhao Wang, Huayou Chen and Xuetao Xu

Energy, 2025, vol. 334, issue C

Abstract: Accurate prediction of crude oil price is significant for energy supply stability, investment decision, and sustainable energy economic development. In fact, even within the same week, crude oil price can fluctuate significantly at different time stages, and point prediction methods cannot capture this phenomenon. Therefore, this paper proposes a multivariate weekly crude oil price interval-valued prediction architecture based on interval decomposition and adaptive integration. Firstly, interval grey incidence analysis (IGIA) was used to select feature interval-valued time series (ITS). Next, multivariate variational mode decomposition (MVMD) was employed to decompose oil price and optimal features ITS simultaneously. Thirdly, an attention weight multi-output least squares support vector regression (AW-MLSSVR) was applied to predict each subseries, respectively. Finally, an adaptive integration (AI) strategy was employed to weight the predictive ITS of each subseries. In this framework, MVMD solves the defect of univariate decomposition techniques that cannot extract multivariate scale common mode features. AW fully learns the similarity between features and targets, while AI addresses the limitation of simple addition as integration operation. The two datasets demonstrated that compared with the optimal interval-valued model, this architecture promoted on the six evaluation indicators, with average reductions of 39.97 %, 37.83 %, 39.06 %, 37.95 %, 5.79 % and 59.85 %, respectively.

Keywords: Crude oil price; Interval-valued prediction; Interval grey incidence analysis; Attention weight; Adaptive integration (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544225034425
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:334:y:2025:i:c:s0360544225034425

DOI: 10.1016/j.energy.2025.137800

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().