A compressed sensing based AI learning paradigm for crude oil price forecasting
A compressed sensing based AI learning paradigm for crude oil price forecasting
复制标题
基于压缩感知的原油价格预测人工智能学习范式
DOI：
10.1016/j.eneco.2014.09.019
复制
发表时间：
2014-11
期刊：
影响因子：
12.8
通讯作者：
Tang, Ling
中科院分区：
文献类型：
--
作者：
Yu, Lean;Zhao, Yang;Tang, Ling
由于原油价格序列的复杂性，传统的基于概率论的预测方法不能产生很好的预测效果。为了提高预测性能，通过将基于压缩感知的去噪（CSD）和一定的人工智能（AI）相结合，提出了一种新的基于压缩感知的学习范式，即，CSD-AI该方法首先对国际原油价格原始数据进行CSD预处理，消除噪声，然后利用一定的人工智能工具对净化后的数据进行预测。特别是，CSD的过程旨在降低污染数据的噪声水平，并进一步提高AI模型的预测性能。为了验证的目的，国际原油价格序列的西得克萨斯中质油（WTI）作为样本数据。实证结果表明，所提出的CSD-AI学习范式在水平和方向精度方面显著优于所有其他基准模型，包括没有CSD过程的单一模型和具有其他去噪技术的混合模型。此外，在不同时间范围的不同数据样本的情况下，所提出的模型表现最好，表明所提出的CSD-AI学习范式是一种有效和鲁棒的原油价格预测方法。
Due to the complexity of crude oil price series, traditional statistics-based forecasting approach cannot produce a good prediction performance. In order to improve the prediction performance, a novel compressed sensing based learning paradigm is proposed through integrating compressed sensing based denoising (CSD) and certain artificial intelligence (AI), i.e., CSD-AI. In the proposed learning paradigm, CSD is first performed as a preprocessor for the original data of international crude oil price to eliminate the noise, and then a certain powerful AI tool is employed to conduct prediction for the cleaned data. In particular, the process of CSD aims to reduce the level of noise which pollutes the data, and to further enhance the prediction performance of the AI model. For verification purpose, international crude oil price series of West Texas Intermediate (WTI) are taken as sample data. Empirical results demonstrate that the proposed CSD-AI learning paradigm significantly outperforms all other benchmark models including single models without CSD process and hybrid models with other denoising techniques, in terms of level and directional accuracies. Furthermore, in the case of different data samples with different time ranges, the proposed model performs the best, indicating that the proposed CSD-AI learning paradigm is an effective and robust approach in crude oil price prediction.
登录
查看更多内容
影响因子：
4.5
作者：
Chunming Yuan
通讯作者：
Chunming Yuan
DOI：
10.1109/9780470544334.ch9
发表时间：
2001
期刊：
Comput. Electron. Agric.
影响因子：
--
作者：
T. Başar
通讯作者：
T. Başar
DOI：
10.2307/2988198
发表时间：
1978-12
期刊：
The Statistician
影响因子：
--
作者：
G. Box;G. Jenkins;G. Reinsel;G. Ljung
通讯作者：
G. Box;G. Jenkins;G. Reinsel;G. Ljung
DOI：
10.1017/cbo9780511794308
发表时间：
2012
期刊：
--
影响因子：
--
作者：
Gitta Kutyniok
通讯作者：
Gitta Kutyniok
DOI：
10.1049/ep.1965.0268
发表时间：
1965
期刊：
Electronics and Power
影响因子：
--
作者：
K. W. Cattermole
通讯作者：
K. W. Cattermole