A multi-scale method for forecasting oil price with multi-factor search engine data
文献类型：期刊论文
| 作者 | Tang, Ling2; Zhang, Chengyuan2; Li, Ling3; Wang, Shouyang1 |
| 刊名 | APPLIED ENERGY
 |
| 出版日期 | 2020 |
| 卷号 | 257页码:12 |
| ISSN号 | 0306-2619 |
| 关键词 | Big data Search engine data Google trends Multivariate empirical mode decomposition Oil price forecasting |
| DOI | 10.1016/j.apenergy.2019.114033 |
| 英文摘要 | With the boom in big data, a promising idea for using search engine data has emerged and improved international oil price prediction, a hot topic in the fields of energy system modelling and analysis. Since different search engine data drive the oil price in different ways at different timescales, a multi-scale forecasting methodology is proposed that carefully explores the multi-scale relationship between the oil price and multi-factor search engine data. In the proposed methodology, three major steps are involved: (1) a multi-factor data process, to collect informative search engine data, reduce dimensionality, and test the predictive power via statistical analyses; (2) multi-scale analysis, to extract matched common modes at similar timescales from the oil price and multi-factor search engine data via multivariate empirical mode decomposition; (3) oil price prediction, including individual prediction at each timescale and ensemble prediction across timescales via a typical forecasting technique. With the Brent oil price as a sample, the empirical results show that the novel methodology significantly outperforms its original form (without multi-factor search engine data and multi-scale analysis), semi-improved versions (with either multi-factor search engine data or multi-scale analysis), and similar counterparts (with other multi-scale analysis), in both the level and directional predictions. |
| 资助项目 | National Science Fund for Outstanding Young Scholars[71622011] ; National Science Fund for Outstanding Young Scholars[71971007] ; National Natural Science Foundation of China[71433001] ; National Program for Support of Top Notch Young Professionals |
| WOS研究方向 | Energy & Fuels ; Engineering |
| 语种 | 英语 |
| 出版者 | ELSEVIER SCI LTD |
| WOS记录号 | WOS:000506574700005 |
| 源URL | [http://ir.amss.ac.cn/handle/2S8OKBNM/50600]  |
| 专题 | 中国科学院数学与系统科学研究院 |
| 通讯作者 | Tang, Ling |
| 作者单位 | 1.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China 2.Beihang Univ, Sch Econ & Management, 37 Xueyuan Rd, Beijing 100191, Peoples R China 3.Capital Univ Econ & Business, Int Sch Econ & Management, Beijing 100070, Peoples R China |
| 推荐引用方式 GB/T 7714 | Tang, Ling,Zhang, Chengyuan,Li, Ling,et al. A multi-scale method for forecasting oil price with multi-factor search engine data[J]. APPLIED ENERGY,2020,257:12. |
| APA | Tang, Ling,Zhang, Chengyuan,Li, Ling,&Wang, Shouyang.(2020).A multi-scale method for forecasting oil price with multi-factor search engine data.APPLIED ENERGY,257,12. |
| MLA | Tang, Ling,et al."A multi-scale method for forecasting oil price with multi-factor search engine data".APPLIED ENERGY 257(2020):12. |
入库方式： OAI收割
来源：数学与系统科学研究院
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