A compressed sensing based AI learning paradigm for crude oil price forecasting

Lean Yu (), Yang Zhao and Ling Tang

Energy Economics, 2014, vol. 46, issue C, 236-245

Abstract: 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.

Keywords: Compressed sensing; Data denoising; Crude oil price prediction; Hybrid model; Feed-forward neural network (search for similar items in EconPapers)
JEL-codes: C45 C53 Q47 (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (46)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0140988314002394
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:46:y:2014:i:c:p:236-245

DOI: 10.1016/j.eneco.2014.09.019

Access Statistics for this article

Energy Economics is currently edited by R. S. J. Tol, Beng Ang, Lance Bachmeier, Perry Sadorsky, Ugur Soytas and J. P. Weyant

More articles in Energy Economics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().