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Energies 2018, 11(11), 3029; https://doi.org/10.3390/en11113029

Energy Commodity Price Forecasting with Deep Multiple Kernel Learning

1
Department of Business Administration, National Changhua University of Education, Changhua 50074, Taiwan
2
School of Business Administration, Hubei University of Economics, Wuhan 430205, China
3
Research Center of Hubei Logistics Development, Hubei University of Economics, Wuhan 430205, China
4
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)
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Abstract

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

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