基于VMD的中国出口集装箱运价指数分析与组合预测
Analysis and combined forecasting of China containerized freight index based on VMD
查看参考文献37篇
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文摘
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基于分解-重构-分项预测-集成思想,通过优选分解方法、优化重构方法、优选预测方法及合理选择集成方法等途径,构建了基于变分模态分解(VMD)的组合预测模型,对中国出口集装箱运价指数(CCFI)进行了预测,分析了CCFI波动特性及经济内涵.首先,选用VMD将运价指数序列分解为多个模态分量;其次,采用C值优化的FCM算法将模态分量重构为高、中、低频和趋势项,通过波动特性分析挖掘了重构项蕴含的短期市场不均衡因素、季节因素、重大事件及市场供需等经济内涵;再次,构建了基于数据特征分析的预测模型优选方法,进行了重构项预测;最后,将重构项预测值相加集成,分析了预测效果.实证结果表明,构建的组合模型预测效果优于BPNN、SVM、ARIMA等单一模型、EMD组合模型及未优化的VMD组合模型,较好地体现了CCFI外在波动特征与内在经济意义. |
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其他语种文摘
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Following the idea of decomposition-reconstruction-subsequence forecasting-ensemble, a combined forecasting model based on variational mode decomposition (VMD) was proposed. The model was constructed by selecting suitable decomposition model, optimizing reconstruction method, choosing appropriate subsequence forecasting method and ensemble method. And it was used to forecast the China containerized freight index (CCFI) and analyze the volatility characteristics and economic connotations of CCFI. Firstly, The time series CCFI was decomposed into multiple modal components by using VMD. Secondly, The modal components were reconstructed into high frequency, medium frequency, low frequency and trend subsequences, which means short-term market imbalance factors, seasonal factors, major events and market supply and demand respectively. Here, the fuzzy C-clustering algorithm was used to reconstruct the modal components, and its parameter C was optimized by component time-scale analysis. The economic meaning of each subsequence was explored by analyzing its volatility characteristics. Thirdly, a method based on data feature analysis was proposed to select the proper forecasting models, and it was used for reconstruct subsequences forecast. Finally, forecast results of reconstructed subsequences were added to obtain final output, and the ensemble forecast output was compared with other models' forecast results. The empirical results showed that the combined forecast model proposed in this paper is superior to the single model, such as BPNN, SVM, ARIMA, and EMD combination model, as well as other multiscale combined forecast models based on VMD. And the analysis results reflected the external fluctuation characteristics and intrinsic economic meaning of CCFI. |
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来源
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系统工程理论与实践
,2021,41(1):176-187 【核心库】
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DOI
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10.12011/setp2019-0226
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关键词
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集装箱运价
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预测
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变分模态分解
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数据特征分析
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模糊聚类
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支持向量机
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神经网络
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地址
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1.
大连海事大学, 综合交通运输协同创新中心, 大连, 116026
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大连海事大学交通运输工程学院, 大连, 116026
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珠海城市职业技术学院经济管理学院, 珠海, 519000
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语种
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中文 |
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文献类型
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研究性论文 |
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ISSN
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1000-6788 |
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学科
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社会科学总论 |
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基金
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国家自然科学基金重点项目
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国家自然科学基金
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国家教育部长江学者与创新团队发展计划
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广东省教育厅项目
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文献收藏号
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CSCD:6890848
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