In order to improve the prediction performance in oil price forecasting, a novel memory-trait-driven decomposition–reconstruction–ensemble learning paradigm is proposed for oil price forecasting. The proposed methodology consists of four steps, i.e., data decomposition for original complex time series, component reconstruction for decomposed components, individual prediction for the reconstructed components, and ensemble output based on the individual component prediction results, which are all driven by memory traits. For verification purpose, the West Texas Intermediate (WTI) crude oil spot prices are used as the sample data. The experimental results demonstrated that the proposed methodology can produce the better and more robust results relative to the benchmarking models listed in this study. This indicates that the proposed memory-trait-driven decomposition–reconstruction–ensemble​ methodology can be used as a promising solution to oil price prediction with the traits of memory.

为了提高油价预测中的预测性能，提出了一种新的记忆特征驱动的分解-重建-集成学习范式用于油价预测。所提出的方法包括四个步骤，即原始复杂时间序列的数据分解、分解成分的成分重构、重构成分的个体预测以及基于个体成分预测结果的集成输出，这些都是由记忆特征驱动的。为验证目的，以西德克萨斯中质原油 (WTI) 原油现货价格作为样本数据。实验结果表明，相对于本研究中列出的基准模型，所提出的方法可以产生更好、更稳健的结果。