KMS Of Academy of mathematics and systems sciences, CAS
| A multi-granularity heterogeneous combination approach to crude oil price forecasting | |
| Wang, Jue1,2; Zhou, Hao1,2; Hong, Tao3; Li, Xiang1,2; Wang, Shouyang1,2 | |
| 2020-09-01 | |
| Source Publication | ENERGY ECONOMICS
 |
| ISSN | 0140-9883 |
| Volume | 91Pages:9 |
| Abstract | Crude oil price forecasting has attracted much attention due to its significance on commodities market as well as nonlinear complexity in prediction task. Combining forecasts in different granular spaces, we propose a multi-granularity heterogeneous combination approach to enhance forecasting accuracy in the study. Firstly, we introduce various feature selection techniques including filter, wrapper and embedded methods, to identify key factors that affect crude oil price and construct different granular spaces. Secondly, distinct feature subsets distinguished by different feature selection methods are incorporated to generate individual forecasts using three popular forecasting models including Linear regression (LR), Artificial neural network (ANN) and Support vector machine (SVR). Finally, the final forecasts are obtained by combining the forecasts from individual forecasting model in each granular space and the optimal weighting vector is achieved by artificial bee colony (ABC) techniques. The experimental results demonstrate that the proposed multi-granularity heterogeneous combination approach based on ABC can outperform not only individual competitive benchmarks but also single-granularity heterogeneous and multi-granularity homogenous approaches. (C) 2020 Elsevier B.V. All rights reserved. |
| Keyword | Crude oil Price Forecast combination Feature selection Multi-granularity Artificial bee colony |
| DOI | 10.1016/j.eneco.2020.104790 |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | National Center for Mathematics and Interdisciplinary Sciences (NCMIS) ; Chinese Academy of Sciences (CAS) ; National Natural Science Foundation of China (NSFC)[71771208] ; National Natural Science Foundation of China (NSFC)[71271202] |
| WOS Research Area | Business & Economics |
| WOS Subject | Economics |
| WOS ID | WOS:000582639700002 |
| Publisher | ELSEVIER |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.amss.ac.cn/handle/2S8OKBNM/52437 |
| Collection | 中国科学院数学与系统科学研究院 |
| Corresponding Author | Hong, Tao |
| Affiliation | 1.Chinese Acad Sci, Acad Math & Syst Sci, MADIS, CEFS, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100190, Peoples R China 3.Univ North Carolina Charlotte, Syst Engn & Engn Management Dept, Charlotte, NC 28223 USA |
| Recommended Citation GB/T 7714 | Wang, Jue,Zhou, Hao,Hong, Tao,et al. A multi-granularity heterogeneous combination approach to crude oil price forecasting[J]. ENERGY ECONOMICS,2020,91:9. |
| APA | Wang, Jue,Zhou, Hao,Hong, Tao,Li, Xiang,&Wang, Shouyang.(2020).A multi-granularity heterogeneous combination approach to crude oil price forecasting.ENERGY ECONOMICS,91,9. |
| MLA | Wang, Jue,et al."A multi-granularity heterogeneous combination approach to crude oil price forecasting".ENERGY ECONOMICS 91(2020):9. |
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