期刊
NEURAL COMPUTING & APPLICATIONS
卷 27, 期 8, 页码 2193-2215出版社
SPRINGER LONDON LTD
DOI: 10.1007/s00521-015-1999-4
关键词
Crude oil price forecasting; Hybrid model; Least squares support vector regression (LSSVR); Grid method; Genetic algorithm (GA); Parameter optimization
资金
- National Science Fund for Distinguished Young Scholars (NSFC) [71025005]
- National Natural Science Foundation of China (NSFC) [91224001, 71301006]
- National Program for Support of Top-Notch Young Professionals
- Fundamental Research Funds for the Central Universities in BUCT
In order to effectively model crude oil spot price with inherently high complexity, a hybrid learning paradigm integrating least squares support vector regression (LSSVR) with a hybrid optimization searching approach for the parameters selection in the LSSVR [consisting of grid method and genetic algorithm (GA)], i.e., a hybrid grid-GA-based LSSVR model, is proposed in this study. In the proposed hybrid learning paradigm, the grid method, a simple but efficient searching method, is first applied to roughly but rapidly determine the proper boundaries of the parameters in the LSSVR; then, the GA, an effective and powerful intelligent searching algorithm, is further implemented to select the most suitable parameters. For illustration and verification, the proposed learning paradigm is used to predict the crude oil spot prices of the West Texas Intermediate and the Brent markets. The empirical results demonstrate that the proposed hybrid grid-GA-based LSSVR learning paradigm can outperform its benchmarking models (including some popular forecasting techniques and similar LSSVRs with other parameter searching algorithms) in terms of both prediction accuracy and time-savings, indicating that it can be utilized as one effective forecasting tool for crude oil price with high volatility and irregularity.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据