Dr. Liyan Geng
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Dr. Liyan Geng

Academic Visitor
Peking University, China

Highest Degree
Ph.D. in Management Science and Engineering from Tianjin University, China

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Area of Interest:

Business Management and Accounting
100%
Information Technology
62%
Intelligent Forecasting
90%
Logistics Demand
75%
Series Analysis
55%

Research Publications in Numbers

Books
0
Chapters
0
Articles
0
Abstracts
0

Selected Publications

  1. Geng, L., Y. Liang, Z. Zhang and X. Shi, 2016. Forecasting range volatility using support vector machines with improved PSO algorithms. TELKOMNIKA: Telecommun. Comput. Elect. Control, 14: 208-216.
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  2. Geng, L., 2016. Intelligent Forecasting Methods for Logistics Demand. Science Press, Beijing.
  3. Geng, L. and Z. Zhang, 2016. Application of ANFIS-Based CARRX model to stock volatility forecasting. Int. J. Simul.: Syst. Sci. Technol., 17: 10.1-10.7.
  4. Geng, L. and B. Guo, 2016. Intelligent forecasting model for Chinese financial volatility and its empirical research. Stat. Decis., 7: 148-151.
  5. Geng, L., 2015. Least squares support vector machines for forecasting stock index volatility. Stat. Decis., 9: 90-92.
  6. Geng, L., 2015. Intelligent Forecasting Model for Financial Volatility and its Empirical Research. Science Press, Beijing.
  7. Geng, L., 2015. Forecast of logistics demand using LSSVM combining GRA with KPCA. J. Transport. Syst. Eng. Inform. Technol., 15: 137-158.
  8. Geng, L. and Z. Zhang, 2015. Forecast of stock index volatility using grey GARCH-type models. Open Cybernetics Syst. J., 9: 93-98.
  9. Geng, L. and B. Guo, 2015. Forecast of logistics demand using DACPSO-LSSVM model. Stat. Decis., 14: 78-81.
  10. Geng, L., 2014. Forecast of stock index volatility using wavelet support vector machines. Adv. Manage. Sci., 3: 19-22.
  11. Geng, L., 2014. Forecast of fund volatility using least squares wavelet support vector regression machines. J. Chem. Pharmaceut. Res., 6: 190-195.
  12. Geng, L. and X.K. Lv, 2013. Logistics demand forecasting using KPCA-based LSSVR with two-order oscillating particle swarm algorithm. J. Applied Sci., 13: 3557-3562.
  13. Geng, L. and L.L. Ding, 2013. Forecasting of logistics demand based on grey correlation analysis and least square SVM. Logist. Technol., 32: 130-135.
  14. Geng, L. and F. Yu, 2013. Forecasting stock volatility using LSSVR-based GARCH model optimized by siwpso algorithm. J. Applied Sci., 13: 5132-5137.
  15. Geng, L.Y., P. Zhao and Z.F. Zhang, 2012. Logistics demand forecasting based on LSSVM optimized by two-order oscillating PSO. Appl. Res. Comput., 29: 2558-2560.
  16. Geng, L.Y. and Q.T. Dong, 2012. Forecast of regional logistics demand using KPCA-based LSSVMs optimized by PSOTVAC. Adv. Inform. Sci. Serv. Sci., 4: 313-319.
  17. Geng, L., T.W. Zhang and P. Zhao, 2012. Forecast of railway freight volumes based on LS-SVM with grey correlation analysis. J. China Railway Soc., 34: 1-6.
  18. Geng, L. and Y.G. Liang, 2012. Prediction on fund volatility based on SVRGM-GARCH model. Adv. Mater. Res., 403-408: 3763-3768.
  19. Geng, L. and Y.G. Liang, 2012. Prediction of railway freight volumes based on grey adaptive particle swarm least squares support vector machine model. J. SouthWest Jiaotong Univ., 47: 144-150.
  20. Geng, L., Z. Zhang and Y.G. Liang, 2011. Freight forecasting based on ANFIS inference system. China Manage. Inform., 14: 60-62.
  21. Geng, L., Y.G. Liang and Z. Zhang, 2011. Forecast on railway freight volumes based on APSO-LSSVM model. China Market, 41: 5-7.
  22. Geng, L. and J.H. Ma, 2009. Volatility forecasting for fund market using grey support vector machine. Applic. Res. Comput., 26: 2471-2473.
  23. Geng, L. and J.H. Ma, 2009. TSK nonlinear combined forecasting model of volatility of Chinese stock markets. Stat. Decis., 1: 123-126.
  24. Geng, L. and J.H. Ma, 2009. RGM-EGARCH model and its empirical research on Shenzhen stock market. Stat. Decis., 6: 143-145.
  25. Geng, L. and J.H. Ma, 2008. Forecasting volatility of stock market based on least squares support vector regression and CARRX model. Stat. Decis., 13: 48-50.