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

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.