| A dynamic ensemble learning with multi-objective optimization for oil prices prediction | |
Hao, Jun1; Feng, Qianqian2,3; Yuan, Jiaxin1; Sun, Xiaolei2 ; Li, Jianping1
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| 发表期刊 | RESOURCES POLICY
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| 关键词 | Ensemble forecasting Dynamic ensemble Time-varying weight Oil price forecasting Multi-objective optimization |
| 摘要 | Accurately predicting oil prices is a challenging task since its complex fluctuation characteristics. This paper innovatively introduces the metabolism mechanism and sliding window technology and proposes a dynamic time-varying weight ensemble prediction model with multi-objective programming to ameliorate the oil price's prediction performance. This paper first adopts the random forest to select and generate the best feature sets. Second, different individual models are selected to build a heterogeneous ensemble prediction framework. Then, a multi-objective weight generation model is established by considering horizontal and directional accuracy. Moreover, the nondominated sorting genetic algorithm-II is utilized to compute the prediction errors of a single model at different stages and achieve model optimization selection and ensemble weight generation. Finally, we take Brent and WTI oil prices as the prediction objects to verify the effectiveness and superiority of the proposed model. The experimental results reveal that the dynamic time-varying weight ensemble forecasting model has excellent prediction capability for oil prices and can become an effective forecasting tool. |
| 2022 | |
| 卷号 | 79 |
| ISSN | 0301-4207 |
| 文章类型 | Article |
| DOI | 10.1016/j.resourpol.2022.102956 |
| 关键词[WOS] | MODEL |
| 语种 | 英语 |
| WOS研究方向 | Environmental Sciences & Ecology |
| WOS类目 | Environmental Studies |
| WOS记录号 | WOS:000862851400002 |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.casisd.cn/handle/190111/12067 |
| 专题 | 系统分析与管理研究所 |
| 作者单位 | 1.Univ Chinese Acad Sci, Sch Econ & Management, Beijing 100190, Peoples R China 2.MOE Social Sci Lab Digital Econ Forecasts & Policy, Beijing 100190, Peoples R China 3.Chinese Acad Sci, Inst Sci & Dev, Beijing 100190, Peoples R China 4.Univ Chinese Acad Sci, Sch Publ Policy & Management, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Hao, Jun,Feng, Qianqian,Yuan, Jiaxin,et al. A dynamic ensemble learning with multi-objective optimization for oil prices prediction[J]. RESOURCES POLICY,2022,79. |
| APA | Hao, Jun,Feng, Qianqian,Yuan, Jiaxin,Sun, Xiaolei,&Li, Jianping.(2022).A dynamic ensemble learning with multi-objective optimization for oil prices prediction.RESOURCES POLICY,79. |
| MLA | Hao, Jun,et al."A dynamic ensemble learning with multi-objective optimization for oil prices prediction".RESOURCES POLICY 79(2022). |
| 条目包含的文件 | 条目无相关文件。 | |||||
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