KMS Of Academy of mathematics and systems sciences, CAS
Interval decomposition ensemble approach for crude oil price forecasting | |
Sun, Shaolong1,2,3; Sun, Yuying1,4; Wang, Shouyang1,2,4; Wei, Yunjie1,4 | |
2018-10-01 | |
发表期刊 | ENERGY ECONOMICS |
ISSN | 0140-9883 |
卷号 | 76页码:274-287 |
摘要 | Crude oil is one of the most important energy sources in the world, and it is very important for policymakers, enterprises and investors to forecast the price of crude oil accurately. This paper proposes an interval decomposition ensemble (IDE) learning approach to forecast interval-valued crude oil price by integrating bivariate empirical mode decomposition (BEMD), interval MLP (MLPI) and interval exponential smoothing method (Holt(I)). Firstly, the original interval-valued crude oil price is transformed into a complex-valued signal. Secondly, BEMD is used to decompose the constructed complex-valued signal into a finite number of complex-valued intrinsic mode functions (IMFs) components and one complex-valued residual component. Thirdly, MLPI is used to simultaneously forecast the lower and the upper bounds of each IMF (non-linear patterns), and Holt(I) is used for modeling the residual component (linear pattern). Finally, the forecasting results of the lower and upper bounds of all the components are combined to generate the aggregated interval-valued output by employing another MLPI as the ensemble tool. The empirical results show that our proposed IDE learning approach with different forecasting horizons and different data frequencies significantly outperforms some other benchmark models by means of forecasting accuracy and hypothesis tests. (C) 2018 Elsevier B.V. All rights reserved. |
关键词 | Bivariate empirical mode decomposition Crude oil price forecasting Interval-valued time series Interval Holt's method Interval neural networks |
DOI | 10.1016/j.eneco.2018.10.015 |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China[71801213] ; National Natural Science Foundation of China[71771208] ; National Natural Science Foundation of China[71642006] |
WOS研究方向 | Business & Economics |
WOS类目 | Economics |
WOS记录号 | WOS:000453498400018 |
出版者 | ELSEVIER SCIENCE BV |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.amss.ac.cn/handle/2S8OKBNM/31916 |
专题 | 系统科学研究所 |
通讯作者 | Wei, Yunjie |
作者单位 | 1.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Econ & Management, Beijing 100190, Peoples R China 3.City Univ Hong Kong, Sch Data Sci, Kowloon, Tat Chee Ave, Hong Kong, Peoples R China 4.Chinese Acad Sci, Ctr Forecasting Sci, Beijing 100190, Peoples R China |
推荐引用方式 GB/T 7714 | Sun, Shaolong,Sun, Yuying,Wang, Shouyang,et al. Interval decomposition ensemble approach for crude oil price forecasting[J]. ENERGY ECONOMICS,2018,76:274-287. |
APA | Sun, Shaolong,Sun, Yuying,Wang, Shouyang,&Wei, Yunjie.(2018).Interval decomposition ensemble approach for crude oil price forecasting.ENERGY ECONOMICS,76,274-287. |
MLA | Sun, Shaolong,et al."Interval decomposition ensemble approach for crude oil price forecasting".ENERGY ECONOMICS 76(2018):274-287. |
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