Oil price forecasting with hierarchical multiple kernel machines

Shian Chang Huang, Lung Fu Chang

研究成果: Conference contribution

摘要

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.

原文English
主出版物標題Proceedings - 2014 International Symposium on Computer, Consumer and Control, IS3C 2014
發行者IEEE Computer Society
頁面260-263
頁數4
ISBN(列印)9781479952779
DOIs
出版狀態Published - 2014 一月 1
事件2nd International Symposium on Computer, Consumer and Control, IS3C 2014 - Taichung, Taiwan
持續時間: 2014 六月 102014 六月 12

出版系列

名字Proceedings - 2014 International Symposium on Computer, Consumer and Control, IS3C 2014

Other

Other2nd International Symposium on Computer, Consumer and Control, IS3C 2014
國家Taiwan
城市Taichung
期間14-06-1014-06-12

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Control and Systems Engineering

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