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Hierarchical combined forecasting based on L1 norm of reconstructed phase points


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Hierarchical combined forecasting based on L1 norm of reconstructed phase points
Lu JianshanWang ChangmingZhang Aijun
School of Mechanical Engineering,NUST,Nanjing 210094,China
combined forecasting phase space reconstruction L1 norm hierarchical structure stabilized platform
To overcome the damage from the system time delay,a new hierarchical combined forecasting method based on L1 norm of reconstructed phase points is proposed using the phase space reconstruction theory and the combined forecasting method.For chaotic system series,the phase space delay coordinate reconstitution theory is applied to reconstruct the phase space,the hierarchical combined forecasting method is used to analyze the change laws of phase point L1 norm,and forecasting values of time series are obtained with the L1 norm inverse solution.The AR(p)model and metabolism GM(1,1)model are employed as single models while the hierarchical structure based the weight determining method is used to ascertain the weights of each single model.To improve the forecasting precision,residual errors are compensated with GM(1,1)model.The algorithm performance evaluation is obtained by simulating the measured data,and the simulating results verify the method effective.


[1] Lee Cheng-chung,Wan Terng-jou,Kuo Chao-yin,et al.Estimating air quality in a traffic tunnel using a forecasting combination model[J].Environmental Monitoring and Assessment,2006,112(1-3):327-345.
[2]Sfetsos A,Siriopoulos C.Combinatorial time series forecasting based on clustering algorithms and neural networks[J].Neural Computing and Applications,2004,13(1):56-64.
[3]Jiang Aihua,Mei Chi,E Jiaqiang,et al.Nonlinear combined forecasting model based on fuzzy adaptive variable weight and its application[J].Journal of Central South University of Technology,2010,17(4):863-867.
Chen Huayou,Liu Chunlin.Properties of weighted geometric means combination forecasting model based on L1 norm[J].Journal of Southeast University(Natural Science Edition),2004,34(4):535-540.
Liu Jinsong.Adaptive combination of multi-step forecast and its application[J].Journal of Jiamusi University(Natural Science Edition),2009,27(4):535-537.
Chen Shuyan,Wang Wei.Grey neural network forecasting for traffic flow[J].Journal of Southeast University(Natural Science Edition),2004,34(4):541-544.
Zhou Quan,Ren Haijun,Li Jian,et al.Variable weight combination method for mid-long term power load forecasting based on hierarchical structure[J].Proceedings of the CSEE,2010,30(16):47-52.
Liu Wei,Wang Kejun,Shao Keyong.Predicting chaotic time series using hybrid particle swarm optimization algorithm[J].Control and Decision,2007,22(5):562-565.
Li Xibing,Liu Zhixiang.Research on grey prediction of deformation laws in backfill based on phase space reconstruction[J].Journal of Safety and Environment,2004,4(6):54-57.
[11]Maguire L P,Roche B,McGinnity T M,et al.Predicting a chaotic time series using a fuzzy neural network[J].Information Sciences,1998,112(1):125-136.
Jiang Tianhan,Shu Jiong.Multi-step prediction of chaotic time series using the least squares support vector machines[J].Control and Decision,2006,21(1):77-80.
[13]Takens F.Determining strange attractors in turbulence[J].Lecture Notes in Mathematics(Berlin:Springer),1981,898:361-381.
Zhang Zhisheng,Sun Yaming,Wang Zhaofeng,et al.A new STLF approach based on the fusion of optimal neighbor points in phase space and the recursive neural network[J].Proceedings of the CSEE,2003,23(8):44-49.
Ma Jie,Li Guobin.Time series prediction of ship rolling[J].Journal of Beijing Institute of Machinery,2006,21(1):4-7.
Gu Xiaohui,Wang Xiaoming,Zhao Youshou.Grey forecast about helicopter tracks[J].Journal of Nanjing University of Science and Technology,2001,25(3):242-246.


Last Update: 2013-04-30