Multi-perspective crude oil price forecasting with a new decomposition-ensemble framework
Multi-perspective crude oil price forecasting with a new decomposition-ensemble framework
复制标题
DOI：
10.1016/j.resourpol.2022.102737
复制
发表时间：
2022-05-02
期刊：
影响因子：
10.2
通讯作者：
Sun, Shaolong
中科院分区：
文献类型：
--
作者：
Guo, Jingjun;Zhao, Zhengling;Sun, Shaolong
原油是全球重要的大宗商品，其价格波动影响一国的政治经济安全。因此，有必要进行原油价格预测。在多源信息和分解集成预测研究的基础上，将两者结合成一个模型，提出了一种新的分解集成框架下的多视角原油价格预测模型。具体来说，通过变分模态分解（VMD）和模糊熵（FE）将原油价格序列分解并重构为多种模态。此外，我们使用格兰杰因果检验从结构化和非结构化多源数据中筛选有效的预测变量，并通过随机森林递归特征消除（RF-RFE）选择最佳输入特征。最后，根据所选的不同输入特征对每种重构模式进行单独预测，并将得到的预测值进行组合和综合；通过误差评价标准对预测结果进行积分得到最终结果。采用西德克萨斯中质原油 (WTI) 每日现货价格来验证我们提出的模型的性能。实证结果表明，与基准模型相比，所提模型能够显着提高预测精度。
Crude oil is an important global commodity, and its price fluctuation affects the political and economic security of a country. Therefore, it is necessary to conduct crude oil price forecasting. Based on the forecasting research of multi-source information and decomposition-ensemble, we combine the two into a model and propose a multi perspective crude oil price forecasting model under a new decomposition-ensemble framework. Specifically, the crude oil price series is decomposed and reconstructed into several modes through variational mode decomposition (VMD) and fuzzy entropy (FE). Further, we screen the effective predictors from structured and unstructured multi-source data using the Granger causality test, and select the optimal input features through random forest recursive feature elimination (RF-RFE). Finally, each reconstruction mode is individually forecasted on the basis of the selected different input features and the forecasting values obtained are combined and integrated; the final result is obtained from the integrating prediction results through the error evaluation criterion. The West Texas Intermediate (WTI) daily spot price is adopted to validate the performance of our proposed model. The empirical results show that compared with the benchmark models, the proposed model can significantly improve forecasting accuracy.