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Application of deep learning model under improved emd in railway transportation investment benefits and national economic attribute analysis


The railway transportation industry is one of the essential factors to promote economic development, so research is made on improving the investment benefit of construction planning of the railway transportation industry, and on this basis, the national emergency attribute is analyzed. A prediction model of railway transportation investment benefits and national economic attributes based on EEMD-LSTM (ensemble empirical mode decomposition—long-short-term memory) model is proposed. The EEMD algorithm is used to decompose the daily investment price of the railway transportation industry to obtain the IMF (intrinsic mode function) with different cycle characteristics. The daily investment price, IMF component, and residual series of the railway transportation industry are taken as input data. The input data are transmitted through the LSTM model to predict the investment price of the next day. The results show that the EEMD-LSTM model can retain the advantages of EEMD and LSTM and meet the accurate prediction of financial data. The model has good performance for the fitting of actual data and forecast data, and the model has the highest prediction accuracy of 0.2964%. In conclusion, the model proposed is a useful model for predicting financial time series. The exploration can provide an absolute theoretical basis for the formulation and planning of investment risk coping strategies of the railway transportation industry and provide particular theoretical support for the national economic attribute and positioning of the railway transportation industry.

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  1. 1.

    Ananth C, Nagarajan K, Kumar V (2017) A smart approach for secure control of railway transportation systems. Soc Sci Electron Publ 117(15):1215–1221

    Google Scholar 

  2. 2.

    Gao Y, Yang L, Li S (2016) Uncertain models on railway transportation planning problem. Appl Math Model 40(7–8):4921–4934

    MathSciNet  Article  Google Scholar 

  3. 3.

    Azadeh A, Salehi V, Kianpour M (2018) Performance evaluation of rail transportation systems by considering resilience engineering factors: Tehran railway electrification system. Transp Lett 10(1):12–25

    Article  Google Scholar 

  4. 4.

    Yin J, Tang T, Yang L et al (2017) Research and development of automatic train operation for railway transportation systems: a survey. Transp Res Part C: Emerging Technol 85:548–572

    Article  Google Scholar 

  5. 5.

    Lin DY, Fang JH, Huang KL (2019) Passenger assignment and pricing strategy for a passenger railway transportation system. Transp Lett 11(6):320–331

    Article  Google Scholar 

  6. 6.

    Ghofrani F, He Q, Goverde RMP et al (2018) Recent applications of big data analytics in railway transportation systems: a survey. Transp Res Part C: Emerging Technol 90:226–246

    Article  Google Scholar 

  7. 7.

    Vickerman R (2018) Can high-speed rail have a transformative effect on the economy? Transp Policy 62:31–37

    Article  Google Scholar 

  8. 8.

    Ke X, Chen H, Hong Y et al (2017) Do China’s high-speed-rail projects promote local economy?—new evidence from a panel data approach. China Econ Rev 44:203–226

    Article  Google Scholar 

  9. 9.

    Wu S, Chong A (2018) Developmental railpolitics: the political economy of China’s high-speed rail projects in Thailand and Indonesia. Contemp Southeast Asia 40(3):503–526

    Article  Google Scholar 

  10. 10.

    Li X, Fan Y, Wu L (2017) CO2 emissions and expansion of railway, road, airline and in-land waterway networks over the 1985–2013 period in China: a time series analysis. Transp Res Part D Transp Environ 57:130–140

    Article  Google Scholar 

  11. 11.

    Andersson H, Hultkrantz L, Lindberg G et al (2018) Economic analysis and Investment priorities in Sweden’s transport sector. J Benefit-Cost Anal 9(1):120–146

    Article  Google Scholar 

  12. 12.

    Fischer T, Krauss C (2018) Deep learning with long short-term memory networks for financial market predictions. Eur J Oper Res 270(2):654–669

    MathSciNet  Article  Google Scholar 

  13. 13.

    Yan H, Ouyang H (2018) Financial time series prediction based on deep learning. Wireless Pers Commun 102(2):683–700

    Article  Google Scholar 

  14. 14.

    Moews B, Herrmann JM, Ibikunle G (2019) Lagged correlation-based deep learning for directional trend change prediction in financial time series. Expert Syst Appl 120:197–206

    Article  Google Scholar 

  15. 15.

    Wei LY (2016) A hybrid ANFIS model based on empirical mode decomposition for stock time series forecasting. Appl Soft Comput 42:368–376

    Article  Google Scholar 

  16. 16.

    Wang J, Wang J (2017) Forecasting stochastic neural network based on financial empirical mode decomposition. Neural Netw 90:8–20

    Article  Google Scholar 

  17. 17.

    Lahmiri S (2016) A variational mode decompoisition approach for analysis and forecasting of economic and financial time series. Expert Syst Appl 55:268–273

    Article  Google Scholar 

  18. 18.

    Nava N, Di Matteo T, Aste T (2018) Dynamic correlations at different time-scales with empirical mode decomposition. Physica A 502:534–544

    Article  Google Scholar 

  19. 19.

    Yaslan Y, Bican B (2017) Empirical mode decomposition based denoising method with support vector regression for time series prediction: a case study for electricity load forecasting. Measurement 103:52–61

    Article  Google Scholar 

  20. 20.

    Zhang N, Lin A, Shang P (2017) Multidimensional k-nearest neighbor model based on EEMD for financial time series forecasting. Physica A 477:161–173

    MathSciNet  Article  Google Scholar 

  21. 21.

    Chong Z, Qin C, Chen Z et al (2019) Estimating the economic benefits of high-speed rail in China: a new perspective from the connectivity improvement. J Transp and Land Use 12(1):287–302

    Article  Google Scholar 

Download references


This work was supported in part by the Research on the major theoretical and practical problems of Social Sciences in Shaanxi Province. Project No.: 2019Z003

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Correspondence to Jia He.

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He, J. Application of deep learning model under improved emd in railway transportation investment benefits and national economic attribute analysis. J Supercomput 77, 8194–8208 (2021).

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  • EEMD
  • LSTM RNN model
  • EEMD-LSTM model
  • Investment benefits of railway transportation
  • National economic attributes