Oil price forecasting with hierarchical multiple kernel machines

Shian Chang Huang, Lung Fu Chang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The dynamics of oil prices are nonlinear and non-stationary. They are also tightly correlated with global financial markets. Traditional models are not very effective in forecasting oil prices. To address the problem, this study employs a new kernel methods-hierarchical multiple kernel machine (HMKM) to solve the problem. Using information from oil, gold, and currency markets. HMKM exploits multiple information sources with strong capability to identify the relevant ones and their apposite kernel representation. Empirical results demonstrate that our new system robustly outperforms traditional neural networks and regression models. The new system significantly reduces the forecasting errors.

Original languageEnglish
Title of host publicationProceedings - 2014 International Symposium on Computer, Consumer and Control, IS3C 2014
PublisherIEEE Computer Society
Pages260-263
Number of pages4
ISBN (Print)9781479952779
DOIs
Publication statusPublished - 2014 Jan 1
Event2nd International Symposium on Computer, Consumer and Control, IS3C 2014 - Taichung, Taiwan
Duration: 2014 Jun 102014 Jun 12

Publication series

NameProceedings - 2014 International Symposium on Computer, Consumer and Control, IS3C 2014

Other

Other2nd International Symposium on Computer, Consumer and Control, IS3C 2014
CountryTaiwan
CityTaichung
Period14-06-1014-06-12

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All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Control and Systems Engineering

Cite this

Huang, S. C., & Chang, L. F. (2014). Oil price forecasting with hierarchical multiple kernel machines. In Proceedings - 2014 International Symposium on Computer, Consumer and Control, IS3C 2014 (pp. 260-263). [6845868] (Proceedings - 2014 International Symposium on Computer, Consumer and Control, IS3C 2014). IEEE Computer Society. https://doi.org/10.1109/IS3C.2014.76