A new approach for Baltic Dry Index forecasting based on empirical mode decomposition and neural networks

doi:10.1057/mel.2015.2

科研成果详情

发表状态	已发表Published
题名	A new approach for Baltic Dry Index forecasting based on empirical mode decomposition and neural networks
作者	Zeng, Qingcheng 1; Qu, Chenrui 1; Ng, Adolf K.Y.2 ; Zhao, Xiaofeng 1
发表日期	2016
发表期刊	Maritime Economics and Logistics
ISSN/eISSN	1479-2931
卷号	18 期号:2 页码:192-210
摘要	In this article, a method based on empirical mode decomposition (EMD) and artificial neural networks (ANN) is developed for Baltic Dry Index (BDI) forecasting. The original BDI series is decomposed into several independent intrinsic mode functions (IMFs) using EMD first. Then the IMFs are composed into three components: short-term fluctuations, effect of extreme events and long-term trend. On the basis of results of decomposition and composition, ANN is used to model each IMF and composed component. Results show that the proposed EMD-ANN method outperforms ANN and VAR. The EMD-based method thus provides a useful technique for dry bulk market analysis and forecasting. © 2016 Macmillan Publishers Ltd.
关键词	dry bulk shipping market empirical mode decomposition artificial neural networks forecasting Baltic Dry Index (BDI)
DOI	10.1057/mel.2015.2
URL	查看来源
收录类别	SSCI
WOS研究方向	Transportation
WOS类目	Transportation
WOS记录号	WOS:000376775600006
引用统计	被引频次：40[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	https://repository.uic.edu.cn/handle/39GCC9TT/2613
专题	个人在本单位外知识产出
作者单位	1.School of Transportation Management, Dalian Maritime University, 1 Linghai Road, Dalian, 116026, China 2.Department of Supply Chain Management, I.H. Asper School of Business, University of Manitoba, Winnipeg, R3T 5V4, MB, Canada
推荐引用方式 GB/T 7714	Zeng, Qingcheng,Qu, Chenrui,Ng, Adolf K.Y.et al. A new approach for Baltic Dry Index forecasting based on empirical mode decomposition and neural networks[J]. Maritime Economics and Logistics, 2016, 18(2): 192-210.
APA	Zeng, Qingcheng, Qu, Chenrui, Ng, Adolf K.Y., & Zhao, Xiaofeng. (2016). A new approach for Baltic Dry Index forecasting based on empirical mode decomposition and neural networks. Maritime Economics and Logistics, 18(2), 192-210.
MLA	Zeng, Qingcheng,et al."A new approach for Baltic Dry Index forecasting based on empirical mode decomposition and neural networks". Maritime Economics and Logistics 18.2(2016): 192-210.