Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm
Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm
复制标题
使用基于 EMD 的神经网络集成学习范式预测原油价格
DOI：
10.1016/j.eneco.2008.05.003
复制
发表时间：
2008-09-01
期刊：
影响因子：
12.8
通讯作者：
Lai, Kin Keung
中科院分区：
文献类型：
--
作者：
Yu, Lean;Wang, Shouyang;Lai, Kin Keung
本文提出了一种基于经验模态分解（EMD）的神经网络集成学习模型，用于世界原油现货价格预测。为此，首先将原始原油现货价格序列分解为有限的、通常较小的内禀模态函数（IMFs）。然后利用三层前馈神经网络（FNN）模型对提取的每一个imf进行建模，从而准确地预测这些imf的趋势。最后，将所有IMF的预测结果与自适应线性神经网络（ALNN）相结合，形成原始原油价格序列的集合输出。为了验证和测试，使用两个主要的原油价格序列，西德克萨斯中质原油（WTI）现货价格和布伦特原油现货价格，来测试所提出的基于emd的神经网络集成学习方法的有效性。所获得的实证结果证明了所提出的基于emd的神经网络集成学习范式的吸引力。(C) 2008 Elsevier B.V.版权所有
In this study, an empirical mode decomposition (EMD) based neural network ensemble learning paradigm is proposed for world crude oil spot price forecasting. For this purpose, the original crude oil spot price series were first decomposed into a finite, and often small, number of intrinsic mode functions (IMFs). Then a three-layer feed-forward neural network (FNN) model was used to model each of the extracted IMFs, so that the tendencies of these IMFs could be accurately predicted. Finally, the prediction results of all IMF's are combined with an adaptive linear neural network (ALNN), to formulate an ensemble output for the original crude oil price Series. For verification and testing, two main crude oil price series, West Texas Intermediate (WTI) crude oil spot price and Brent crude oil spot price, are used to test the effectiveness of the proposed EMD-based neural network ensemble learning methodology. Empirical results obtained demonstrate attractiveness of the proposed EMD-based neural network ensemble learning paradigm. (C) 2008 Elsevier B.V. All rights reserved.