基于VMD-GRGC-FFT的BDI指数周期特性研究

›› 2017, Vol. 29 ›› Issue (4): 213-225.

基于VMD-GRGC-FFT的BDI指数周期特性研究

余方平, 匡海波

大连海事大学综合交通运输协同创新中心, 大连 116026

收稿日期:2016-08-29 出版日期:2017-04-28 发布日期:2017-04-21
作者简介:余方平,大连海事大学综合交通运输协同创新中心博士研究生;匡海波,大连海事大学综合交通运输协同创新中心教授,博士生导师,博士后
基金资助:
国家自然科学基金项目（71672016）；长江学者和创新团队发展计划资助项目（IRT13048）；河北省交通运输厅重点项目（ZJT2015037）

The Detection of BDI Index Hidden Periodicities: A VMD-GRGC-FFT Ensemble Methods

Yu Fangping, Kuang Haibo

Collaborative Innovation Center for Transport Studies, Dalian Maritime University, Dalian 116026

Received:2016-08-29 Online:2017-04-28 Published:2017-04-21

摘要/Abstract

摘要：

本文对BDI指数周期特性进行了深入探讨，主要创新点有：一是提出了BDI指数周期划分和形成机制的理论分析框架，分析了需求侧、供给侧和非经济三个维度的影响因素综合促成的BDI指数周期波动。二是构建了基于VMD-GRGC-FFT的BDI指数长、中和短周期测算分析框架。借助变分模态分解法（VMD）对BDI指数进行模态分量分解，采用灰色关联度聚类分析法（GRGC）将模态分量重构合成低频、中频和高频三个新分量，以及通过快速傅立叶变换（FFT）模型全面测算BDI指数新分量对应的平均周期。三是采集1985-2015年BDI指数频度日/周/月的数据进行实证分析，结果表明：BDI指数的长周期约16年、中周期2-4年、短周期0.4-0.6年；在2016年至未来几年的时期内，BDI指数仍将处于长周期的萧条阶段和中周期的回复阶段。

关键词: BDI指数, 周期特性, 变分模态分解（VMD）, 灰色关联度聚类分析（GRGC）, 快速傅立叶变换（FFT）

Abstract:

It is significant to find out the periodic characteristics of BDI index, the benchmark index of global shipping market, in order to further understand the law of marine freight rate, forecast the trend of global shipping market and so on. In this study, the BDI index hidden periodicities are discussed in depth, and the main innovation points are: Firstly, the theoretical analysis framework of BDI index periodicities division and formation mechanism is proposed, and the BDI index periodicities fluctuation influence factors of three dimensions: demand side, supply side and non-economy factors, are analyzed. Secondly, the framework of BDI index long period, middle period and short period calculation based on VMD-GRGC-FFT is constructed. BDI index modal components are decomposed by means of Variational Mode Decomposition (VMD), the three synthesis components of low, medium and high frequency are reconstructed using Grey Relational Grades Cluster analysis (GRGC), and hidden average periodicities corresponding to the BDI index are detected through the Fast Fourier transform (FFT) model. Thirdly, we collect BDI index day/week/month data in 1985-2015 for empirical analysis. Results show that: the BDI index's long period is about 16 years, middle period is about 2-4 years and short period is about 0.4-0.6 years. At the same time, we predict that in the next few years after 2016, the BDI index will still be in the long period of the depression stage and the middle period of the recovery stage.

Key words: BDI index, periodicity, Variational Mode Decomposition (VMD), Grey Relational Grades Cluster (GRGC), Fast Fourier Transformation (FFT)

余方平, 匡海波. 基于VMD-GRGC-FFT的BDI指数周期特性研究[J]. , 2017, 29(4): 213-225.

Yu Fangping, Kuang Haibo. The Detection of BDI Index Hidden Periodicities: A VMD-GRGC-FFT Ensemble Methods[J]. , 2017, 29(4): 213-225.

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