Next Article in Journal
Analysis and Control of Electrolytic Capacitor-Less LED Driver Based on Harmonic Injection Technique
Next Article in Special Issue
Computational Modelling of Three Different Sub-Boundary Layer Vortex Generators on a Flat Plate
Previous Article in Journal
Evaluation of the Air Oxygen Enrichment Effects on Combustion and Emissions of Natural Gas/Diesel Dual-Fuel Engines at Various Loads and Pilot Fuel Quantities
Previous Article in Special Issue
Crop Characteristics of Aquatic Macrophytes for Use as a Substrate in Anaerobic Digestion Plants—A Study from Germany
Article Menu
Issue 11 (November) cover image

Export Article

Open AccessArticle
Energies 2018, 11(11), 3029;

Energy Commodity Price Forecasting with Deep Multiple Kernel Learning

Department of Business Administration, National Changhua University of Education, Changhua 50074, Taiwan
School of Business Administration, Hubei University of Economics, Wuhan 430205, China
Research Center of Hubei Logistics Development, Hubei University of Economics, Wuhan 430205, China
Institute for Development of Cross-Strait Small and Medium Enterprise, Wuhan 430205, China
Authors to whom correspondence should be addressed.
Received: 12 September 2018 / Revised: 31 October 2018 / Accepted: 1 November 2018 / Published: 5 November 2018
(This article belongs to the Special Issue 10 Years Energies - Horizon 2028)
Full-Text   |   PDF [507 KB, uploaded 5 November 2018]   |  


Oil is an important energy commodity. The difficulties of forecasting oil prices stem from the nonlinearity and non-stationarity of their dynamics. However, the oil prices are closely correlated with global financial markets and economic conditions, which provides us with sufficient information to predict them. Traditional models are linear and parametric, and are not very effective in predicting oil prices. To address these problems, this study developed a new strategy. Deep (or hierarchical) multiple kernel learning (DMKL) was used to predict the oil price time series. Traditional methods from statistics and machine learning usually involve shallow models; however, they are unable to fully represent complex, compositional, and hierarchical data features. This explains why traditional methods fail to track oil price dynamics. This study aimed to solve this problem by combining deep learning and multiple kernel machines using information from oil, gold, and currency markets. DMKL is good at exploiting multiple information sources. It can effectively identify the relevant information and simultaneously select an apposite data representation. The kernels of DMKL were embedded in a directed acyclic graph (DAG), which is a deep model and efficient at representing complex and compositional data features. This provided a solid foundation for extracting the key features of oil price dynamics. By using real data for empirical testing, our new system robustly outperformed traditional models and significantly reduced the forecasting errors. View Full-Text
Keywords: multiple kernel learning; deep representation; artificial intelligence; energy market; machine learning; time series forecasting multiple kernel learning; deep representation; artificial intelligence; energy market; machine learning; time series forecasting

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Huang, S.-C.; Wu, C.-F. Energy Commodity Price Forecasting with Deep Multiple Kernel Learning. Energies 2018, 11, 3029.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top