交通运输工程

基于VMD-FFT-LSTM模型的BDI指数预测

  • 武华华 ,
  • 匡海波 ,
  • 宋扬
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  • (大连海事大学 a. 综合交通运输协同创新中心; b. 交通运输工程学院, 辽宁  大连  116026)
武华华(1987 — ),男,博士生,E-mail:wu_invictus@163.com.

收稿日期: 2019-01-31

  修回日期: 2019-05-15

  网络出版日期: 2019-05-15

基金资助

国家自然科学基金资助项目(71831002;71672016);长江学者和创新团队发展计划资助(IRT_17R13);辽宁省高等教育内涵发展专项资金(20110117406).

Prediction of BDI based on VMD-FFT-LSTM model

  • WU Hua-hua ,
  • KUANG Hai-bo ,
  • SONG Yang
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  • (a. Collaborative Innovation Center for Transport Studies;b. Transportation Engineering College,Dalian Maritime University, Dalian 116026, China)

Received date: 2019-01-31

  Revised date: 2019-05-15

  Online published: 2019-05-15

摘要

为提升非线性BDI指数的预测效果,分析了多种预测模型对BDI指数的单步及多步预测结果,借助“分解—重构—预测”思路,设计构建了VMD-FFT-LSTM组合预测模型.首先,通过VMD算法分解出BDI指数的IMF分量;然后,结合BDI指数周期理论与FFT算法计算的周期结果重构IMF,达到降噪的目的;最后,运用LSTM模型对重构序列进行多步预测.对比多步预测结果,VMD-FFT-LSTM组合模型预测结果在精度及稳定性上表现更好,解决了SVR模型多步预测结果易在序列局部极值点处出现较大偏差的局限性问题.

本文引用格式

武华华 , 匡海波 , 宋扬 . 基于VMD-FFT-LSTM模型的BDI指数预测[J]. 大连海事大学学报, 2019 , 45(3) : 9 -16 . DOI: 10.16411/j.cnki.issn1006-7736.2019.03.002

Abstract

To improve the prediction effect of non-linear BDI, the single-step and multi-step prediction results of various prediction models for BDI index were analyzed,and the VMD-FFT-LSTM combination prediction model was constructed based on the idea of "decomposition-reconstruction-prediction". Firstly, IMF components of BDI were decomposed by using VMD algorithm. Then, the IMF was reconstructed based on the BDI exponential cycle theory and the periodic results calculated by the FFT algorithm to achieve the purpose of noise reduction. Finally, the LSTM model was used for multi-step prediction of reconstruction sequences. Compared with the multi-step prediction results, it was shown that the prediction results of the VMD-FFT-LSTM combined model have better accuracy and stability,which solved the limitation of SVR model that the multi-step prediction results are prone to large deviation at the local extreme points of the sequence.

参考文献

[1]Lin F,Sim N C S.Trade,income and the Baltic Dry Index[J].European Economic Review, 2013, 59(4):1-18 [2]Kevin C.A short-term adaptive forecasting model for BIFFEX speculation:a Box—Jenkins approach[J].Maritime Policy & Management, 1992, 19(2):91-114 [3]Haigh M S.Cointegration,unbiased expectations,and forecasting in the BIFFEX freight futures market[J].Journal of Futures Markets, 2000, 20(6):545-571 [4]Batchelor R,Alizadeh A,Visvikis I.Forecasting spot and forward prices in the international freight market[J].International Journal of Forecasting, 2007, 23(1):101-114 [5]Duru O.A fuzzy integrated logical forecasting model for dry bulk shipping index forecasting: An improved fuzzy time series approach[J].Expert Systems with Applications, 2010, 37(7):5372-5380 [6] Lin Y J,Wang C C.The dynamic analysis of Baltic exchange dry index [J].[J].International Mathematical Forum,, 2014,, 9::803--823. [7] Egrioglu E.PSO-based high order time invariant fuzzy time series method:Application to stock exchange data [J].[J].Economic Modelling,, 2014,, 38::633--639. [8]Tsioumas V,Papadimitriou S,Smirlis Y,et al.A Novel Approach to Forecasting the Bulk Freight Market[J].Asian Journal of Shipping & Logistics, 2017, 33(1):33-41 [9]Jun Li, Michael G.ParsonsForecasting tanker freight rate using neural networks[J].Maritime Policy & Management, 1997, 24(1):9-30 [10]董良才,黄有方,胡颢.基于模糊神经网络的航运运价指数预测[J].大连海事大学学报, 2010, 36(4):31-34 [11]Dong L, Huang Y, Hu H.Shipping freight index forecasting based on fuzzy neural network[J].Journal of Dalian Maritime University, 2010, 36(4):31-34 [12]Yang Z,Jin L,Wang M.Forecasting Baltic Panamax Index with Support Vector Machine[J].Journal of Transportation Systems Engineering & Information Technology, 2011, 11(3):50-57 [13]Fan S,Ji T,Gordon W, et al.Forecasting Baltic Dirty Tanker Index by Applying Wavelet Neural Networks[J].Journal of Transportation Technologies, 2013, 3(1):68-87 [14]Han Q,Yan B,Ning G,et al.Forecasting Dry Bulk Freight Index with Improved SVM[J].Mathematical Problems in Engineering, 2014, 2014(1):1-12 [15] Lyridis D V,Manos N D,Zacharioudakis P G.Modeling the dry bulk shipping market using macroeconomic factors in addition to shipping market parameters via artificial neural networks [J]. [J].Articles,, 2015,, 41.:- [16]Zeng Q,Qu C,Ng A K Y,et al.A new approach for Baltic Dry Index forecasting based on empirical mode decomposition and neural networks[J].Maritime Economics & Logistics, 2016, 18(2):192-210 [17] Guan F,Peng Z et al.Multi-Step Hybrid Prediction Model of Baltic Supermax Index Based on Support Vector Machine [J]. [J]., 2016,, 26((3): ):219--232. [18] Schmidhuber J.Deep Learning in neural networks: An overview. [J]. [J].Neural Netw,, 2014,, 61:: 85--117. [19] Li X,Du N,Li H,et al.A Deep Learning Approach to Link Prediction in Dynamic Networks [M]// Proceedings of the 2014 SIAM International Conference on Data Mining. 2014: 225-230. [20]Deng L,Yu D.Deep Learning: Methods and Applications[J].Foundations & Trends in Signal Processing, 2014, 7(3):197-387 [21] Honchar O,Persio LD.Artificial neural networks approach to the forecast of stock market price movements [J].[J].International Journal of Economics and Management Systems,, 2016,, 1: :157--162. [22] Gensler A,Henze J,Sick B,et al.Deep Learning for solar power forecasting- An approach using AutoEncoder and LSTM Neural Networks [C]// IEEE International Conference on Systems,Man,and Cybernetics. IEEE,2017: 002858-002865. [23] Abdel-Nasser M,Mahmoud K.Accurate photovoltaic power forecasting models using deep LSTM-RNN [J]. [J].Neural Computing & Applications,, 2017, (10): :1--14. [24] Kumar J,Goomer R,Singh A K.Long Short Term Memory Recurrent Neural Network (LSTM-RNN) Based Workload Forecasting Model For Cloud Datacenters [J].[J].Procedia Computer Science,, 2018,, 125: :676--682. [25]Dragomiretskiy K,Zosso D.Variational Mode Decomposition[J].IEEE Transactions on Signal Processing, 2014, 62(3):531-544 [26]Xue Y J,Cao J X,Wang D X,et al.Application of the Variational-Mode Decomposition for Seismic Time–frequency Analysis[J].IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 2017, 9(8):3821-3831 [27]刘尚坤,唐贵基.改进的方法及其在转子故障诊断中的应用[J].动力工程学报, 2016, 36(6):448-453 [28]Liu S,Tang G.Application of Improved VMD Method in Fault Diagnosis of Rotor Systems[J].Journal of Chinese Society of Power Engineering, 2016, 36(6):448-453 [29]张亚超,刘开培,秦亮.基于-和机器学习算法的短期风电功率多层级综合预测模型[J].电网技术, 2016, 40(5):1334-1340 [30]Zhang Y,Liu K,Qin L.Short-Term Wind Power Multi-Leveled Combined Forecasting Model Based on Variational Mode Decomposition-Sample Entropy and Machine Learning Algorithms[J].Power System Technology, 2016, 40(5):1334-1340 [31]余方平,匡海波.基于VMD-GRGC-FFT的BDI指数周期特性研究 [J].[J].管理评论,, 2017, (4):: 213--225. [32]Yu F,Kuang H.The Detection of BDI Index Hidden Periodicities: A VMD-GRGC-FFT Ensemble Methods [J]. [J].Management Review,, 2017, (4): :213--225. [33]Gers F A,Schmidhuber J,Cummins F.Learning to forget: continual prediction with LSTM[J].Neural Computation, 2000, 12(10):850-855 [34] Graves A,Mohamed A R,Hinton G.Speech recognition with deep recurrent neural networks [C] //IEEE International Conference on Acoustics,Speech and Signal Processing. IEEE,2013: 6645-6649. [35]武华华,匡海波,孟斌,等.基于EMD-WA模型的BDI指数波动周期特征研究[J].系统工程理论与实践, 2018, 38(6):1586-1598 [36]Wu H H,Kuang H B,Meng B,et al.Study on the periodic characteristics of BDI index based on EMD-WA model[J].Systems Engineering-Theory& Practice, 2018, 38(6):1586-1598
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