EMD-Based Recurrent Neural Network with Adaptive Regrouping for Port Cargo Throughput Prediction

  • Yan Li
  • Ryan Wen LiuEmail author
  • Quandang MaEmail author
  • Jingxian Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11301)


Accurate prediction of port cargo throughput (PCT) plays an important role in economic investment, transportation planning, port planning and design, etc. PCT time series have the properties of nonlinearity and complexity. To guarantee high-quality prediction performance, we propose to first adopt the empirical mode decomposition (EMD) to decompose the original PCT time series into high and low frequency components. It is more difficult to predict some components due to their properties of weak mathematical regularity. To take advantage of the selfsimilarities within components, each component will be divided into several small parts which are adaptive regrouped (ARG) via the standardized euclidean distance (SED)-based similarity measure. The regrouped parts are then selected to form the training dataset for long short-term memory (LSTM) to enhance the prediction accuracy of each component. The final prediction result can be obtained by integrating the predicted components. Our proposed three-step prediction framework (called EMD-ARG-LSTM) benefits from the property decomposition and adaptive similarity regrouping. Experimental results have illustrated the superior performance of the proposed method in terms of both prediction accuracy and robustness.


Port cargo throughput Prediction Long short-term memory Empirical mode decomposition Similarity regrouping 



This work was supported by National Natural Science Foundation of China (Nos.: 51609195 and 51479156), Fund of Hubei Key Laboratory of Transportation Internet of Things (No.: WHUTIOT-2017B003), and Independent Innovation Research Funding for Undergraduates (No.: 2018-HY-A1-01).


  1. 1.
    Zhang, C., Huang, L., Zhao, Z.: Research on combination forecast of port cargo throughput based on time series and causality analysis. J. Ind. Eng. Manag. 6(1), 124–134 (2013)Google Scholar
  2. 2.
    Fjodorova, N., et al.: Quantitative and qualitative models for carcinogenicity prediction for non-congeneric chemicals using CP ANN method for regulatory uses. Mol. Divers. 14(3), 581–594 (2010)CrossRefGoogle Scholar
  3. 3.
    Hindmarsh, M., Huber, S.J., Rummukainen, K., Weir, D.J.: Numerical simulations of acoustically generated gravitational waves at a first order phase transition. Phys. Rev. D 92(12), 24–30 (2015)CrossRefGoogle Scholar
  4. 4.
    Dixon, W.E., Walker, I.D., Dawson, D.M., Hartranft, J.P.: Fault detection for robot manipulators with parametric uncertainty: a prediction-error-based approach. IEEE Trans. Robotic. Autom. 16(6), 689–699 (2000)CrossRefGoogle Scholar
  5. 5.
    Khosravi, A., Nahavandi, S.: Combined nonparametric prediction intervals for wind power generation. IEEE Trans. Sustain. Energy 4(4), 849–856 (2013)CrossRefGoogle Scholar
  6. 6.
    Preacher, K.J., Curran, P.J., Bauer, D.J.: Computational tools for probing interactions in multiple linear regression, multilevel modeling, and latent curve analysis. J. Educ. Behav. Stat. 31(4), 437–448 (2006)CrossRefGoogle Scholar
  7. 7.
    Hyndman, R.J., Koehler, A.B., Snyder, R.D., Grose, S.: A state space framework for automatic forecasting using exponential smoothing methods. Int. J. Forecast. 18(3), 439–454 (2002)CrossRefGoogle Scholar
  8. 8.
    Wang, C.N., Phan, V.T.: An improvement the accuracy of grey forecasting model for cargo throughput in international commercial ports of Kaohsiung. Int. J. Bus. Econ. Res. 3(1), 1–5 (2014)CrossRefGoogle Scholar
  9. 9.
    Liu, R.W., Chen, J., Liu, Z., Li, Y., Liu, Y., Liu, J.: Vessel traffic flow separation-prediction using low-rank and sparse decomposition. In: IEEE ITSC, pp. 1–6 (2017)Google Scholar
  10. 10.
    Odom, M.D., Sharda, R.: Stock market prediction system with modular neural networks. In: IEEE IJCNN, pp. 1–6 (1990)Google Scholar
  11. 11.
    Ping, F.F., Fang, X.F.: Multivariant forecasting mode of Guangdong province port throughput with genetic algorithms and back propagation neural network. Procedia Soc. Behav. 96, 1165–1174 (2013)CrossRefGoogle Scholar
  12. 12.
    Lv, Y., Duan, Y., Kang, W., Li, Z., Wang, F.Y.: Traffic flow prediction with big data: a deep learning approach. IEEE Trans. Intell. Transp. Syst. 16(2), 865–873 (2015)Google Scholar
  13. 13.
    Connor, J.T., Martin, R.D., Atlas, L.E.: Recurrent neural networks and robust time series prediction. IEEE Trans. Nerual Netw. 5(2), 240–254 (1994)CrossRefGoogle Scholar
  14. 14.
    Ma, X., Tao, Z., Wang, Y., Yu, H., Wang, Y.: Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Transport. Res. C-Emer. 54, 187–197 (2015)CrossRefGoogle Scholar
  15. 15.
    Naik, J., Satapathy, P., Dash, P.K.: Short-term wind speed and wind power prediction using hybrid empirical mode decomposition and kernel ridge regression. Appl. Soft Comput. 70, 1167–1188 (2018)CrossRefGoogle Scholar
  16. 16.
    Liu, S., Xu, L., Li, D.: Multi-scale prediction of water temperature using empirical mode decomposition with back-propagation neural networks. Comput. Electr. Eng. 49, 1–8 (2016)CrossRefGoogle Scholar
  17. 17.
    Yu, L., Wang, S., Lai, K.K.: Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm. Energ. Econ. 30(5), 2623–2635 (2008)CrossRefGoogle Scholar
  18. 18.
    Wang, J., Zhang, W., Li, Y., Wang, J., Dang, Z.: Forecasting wind speed using empirical mode decomposition and Elman neural network. Appl. Soft Comput. 23, 452–459 (2014)CrossRefGoogle Scholar
  19. 19.
    Bianconi, F., Fernández, A.: Evaluation of the effects of Gabor filter parameters on texture classification. Pattern Recogn. 40(12), 3325–3335 (2007)CrossRefGoogle Scholar
  20. 20.
    Chen, P.Y., Lai, Y.C., Zheng, J.Y.: Hardware design and implementation for empirical mode decomposition. IEEE Trans. Ind. Electron. 63(6), 3686–3694 (2016)CrossRefGoogle Scholar
  21. 21.
    Flandrin, P., Rilling, G., Goncalves, P.: Empirical mode decomposition as a filter bank. IEEE Sig. Process. Lett. 11(2), 112–114 (2004)CrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.Hubei Key Laboratory of Inland Shipping Technology, School of NavigationWuhan University of TechnologyWuhanChina
  2. 2.Hubei Key Laboratory of Transportation Internet of Things, School of Computer Science and TechnologyWuhan University of TechnologyWuhanChina

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