Work

DKformer: A Novel Transformer‐Based Model for Interval‐Valued Crude Oil Price Forecasting

Year: 2025

Type: article

Abstract: ABSTRACT Most deep learning techniques are designed for point‐valued data, while interval‐valued data contain richer information over the same time period, especially during extreme events. To leverage this, we propose DKformer, a novel transformer architecture that models interval data as an inseparable set using the loss function. By combining transformers and loss, DKformer learns complex nonstationary and nonlinear patterns in interval data while accounting for uncertainties and dependencies. Compared to existing interval and point‐based benchmark models, DKformer significantly enhances crude oil price forecast accuracy. Remarkably, it demonstrates robust out‐of‐sample prediction performance across various forecast horizons. Furthermore, the effectiveness of DKformer is preserved even under extreme conditions, including Black Swan events. Our work highlights the potential for DKformer to enhance decision‐making in energy and finance, where robustness to uncertainty and the ability to handle extreme events hold paramount importance.

Source: Journal of Forecasting

DOI: 10.1002/for.70070

Authors: Chuanmiao Yan, Xinyu Zhang, Ruhong Cui, Yuying Sun, Shouyang Wang

Institutions: Chinese Academy of Sciences, Academy of Mathematics and Systems Science, University of Chinese Academy of Sciences, ShanghaiTech University

Language: English

Topic: Stock Market Forecasting Methods

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: bronze

Funder: National Natural Science Foundation of China

Award: G2676361859