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.
Highlights
Crude oil is the world’s largest energy commodity and is actively traded internationally.The welfare of oil-importing and oil-producing economies are heavily influenced by fluctuations in oil prices, especially when they are unexpectedly large and persistent
This study focused on developing advanced techniques in oil price forecasting, which is one basis for implementing an effecting hedging or trading strategy
The success of the proposed forecasting model was derived from the combination of multiple kernel machines and deep kernel representation
Summary
Crude oil is the world’s largest energy commodity and is actively traded internationally. The welfare of oil-importing and oil-producing economies are heavily influenced by fluctuations in oil prices, especially when they are unexpectedly large and persistent. As indicated by Abosedra and Baghestani [1], “sharp increases in crude oil prices adversely influence economic growth and accelerate inflation for oil importing economies. Large fall in crude oil prices will generate serious budgetary deficit problems for oil exporting countries”. Accurate oil price forecasting is appealing and important. Many researchers have tried to develop models to maximize forecasting accuracy. Until now, they have not achieved a satisfactory level of performance from their models. The failure of traditional approaches is derived from their model setting. The model forms adopted are usually linear and parametric (Atsalakis and Valavanis [2,3], Fan and Li [4]), which are not flexible enough
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