Dr. Liyan Geng
Academic VisitorPeking University, China
Highest Degree
Ph.D. in Management Science and Engineering from Tianjin University, China
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Selected Publications
- 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.
Direct Link | - Geng, L., 2016. Intelligent Forecasting Methods for Logistics Demand. Science Press, Beijing.
- 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.
- Geng, L. and B. Guo, 2016. Intelligent forecasting model for Chinese financial volatility and its empirical research. Stat. Decis., 7: 148-151.
- Geng, L., 2015. Least squares support vector machines for forecasting stock index volatility. Stat. Decis., 9: 90-92.
- Geng, L., 2015. Intelligent Forecasting Model for Financial Volatility and its Empirical Research. Science Press, Beijing.
- Geng, L., 2015. Forecast of logistics demand using LSSVM combining GRA with KPCA. J. Transport. Syst. Eng. Inform. Technol., 15: 137-158.
- Geng, L. and Z. Zhang, 2015. Forecast of stock index volatility using grey GARCH-type models. Open Cybernetics Syst. J., 9: 93-98.
- Geng, L. and B. Guo, 2015. Forecast of logistics demand using DACPSO-LSSVM model. Stat. Decis., 14: 78-81.
- Geng, L., 2014. Forecast of stock index volatility using wavelet support vector machines. Adv. Manage. Sci., 3: 19-22.
- Geng, L., 2014. Forecast of fund volatility using least squares wavelet support vector regression machines. J. Chem. Pharmaceut. Res., 6: 190-195.
- 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.
- 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.
- Geng, L. and F. Yu, 2013. Forecasting stock volatility using LSSVR-based GARCH model optimized by siwpso algorithm. J. Applied Sci., 13: 5132-5137.
- 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.
- 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.
- 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.
- Geng, L. and Y.G. Liang, 2012. Prediction on fund volatility based on SVRGM-GARCH model. Adv. Mater. Res., 403-408: 3763-3768.
- 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.
- Geng, L., Z. Zhang and Y.G. Liang, 2011. Freight forecasting based on ANFIS inference system. China Manage. Inform., 14: 60-62.
- Geng, L., Y.G. Liang and Z. Zhang, 2011. Forecast on railway freight volumes based on APSO-LSSVM model. China Market, 41: 5-7.
- Geng, L. and J.H. Ma, 2009. Volatility forecasting for fund market using grey support vector machine. Applic. Res. Comput., 26: 2471-2473.
- Geng, L. and J.H. Ma, 2009. TSK nonlinear combined forecasting model of volatility of Chinese stock markets. Stat. Decis., 1: 123-126.
- Geng, L. and J.H. Ma, 2009. RGM-EGARCH model and its empirical research on Shenzhen stock market. Stat. Decis., 6: 143-145.
- 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.