DMEformer: A newly designed dynamic model ensemble transformer for crude oil futures prediction

Crude oil futures prediction plays an important role in ensuring sustainable energy development. However, the performance of existing models is not satisfactory, which limits its further application. The poor performance mainly results from the lack of data mining of economic models and the poor stability of most data analysis models. To solve the above problems, this paper proposes a new dynamic model ensemble transformer (DMEformer). The model uses three different Transformer variants as base models. It not only ensures the difference of base models but also makes the prediction results of base models not to appear disparity. In addition, NSGA-II is adopted to ensemble the results of base models, which considers both the modeling stability and accuracy in the optimization. Finally, the proposed model adopts a dynamic ensemble scheme, which could timely adjust the weight vector according to the fluctuation of energy futures. It further improves the reliability of the model. Comparative experiments from the perspective of single models and ensemble models are also designed. The following conclusions can be drawn from the experimental results: (1) The proposed dynamic ensemble method can improve the performance of the base model and traditional static ensemble method by 16% and 5% respectively. (2) DMEformer can achieve better performance than 20 other models, and its accuracy and MAPE values were 72.5% and 2.8043%, respectively. (3) The proposed model can accurately predict crude oil futures, which provides effective support for energy regulation and sustainable development.