Volume 35 Issue 12
Dec.  2009
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Lü Yongle, Lang Rongling, Tan Zhanzhonget al. Weight allocation of combination prediction based on sequence relative nearness degree[J]. Journal of Beijing University of Aeronautics and Astronautics, 2009, 35(12): 1434-1437. (in Chinese)
Citation: Lü Yongle, Lang Rongling, Tan Zhanzhonget al. Weight allocation of combination prediction based on sequence relative nearness degree[J]. Journal of Beijing University of Aeronautics and Astronautics, 2009, 35(12): 1434-1437. (in Chinese)

Weight allocation of combination prediction based on sequence relative nearness degree

  • Received Date: 12 Nov 2008
  • Publish Date: 31 Dec 2009
  • Aiming at the weight allocation problems of combination prediction for a time series, a new method was proposed to evaluate the applicability of the employed models and allocate weights, based on the "nearness" between the test sequence and the corresponding prediction value sequence, which overcame the shortages of existing methods such as mean square error reciprocal weight (1/MSE), entropy weight and optimization weight. The definitions of sequence relative nearness degree (SRND), related sequence trend association and scale interval entropy were given and well discussed, as well as the weight allocation expressions based on SRND. By the example which combined the autoregressive moving average model, functional-coefficient autoregressive model and radial basis function prediction networks in the prediction analysis for the takeoff exhaust gas temperature margin time series, the conclusion is drawn that the prediction accuracy can be effectively improved with the proposed method, compared to 1/MSE and entropy weight methods, while the calculation mount is far lower than optimization weight method.

     

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  • [1] Bates J M, Granger C W J. Combination of forecasts[J]. Operations Research Quarterly, 1969, 20(4): 451-468 [2] 陈华友. 熵值法及其在确定组合预测权系数中的应用[J]. 安徽大学学报:自然科学版, 2003, 27(4): 1-6 Chen Huayou. Entropy method and application to determine weights of combination forecasting[J]. Journal of Anhui Univ Natural Science Edition, 2003, 27(4): 1-6(in Chinese) [3] 马永开, 唐小我. 线性组合预测模型优化问题研究[J]. 系统工程理论与实践, 1998(9): 110-114 Ma Yongkai, Tang Xiaowo. Research on the problem of optimizing linear combination forecasting model[J]. Systems Engineering-Theory & Practice,1998(9): 110-114(in Chinese) [4] Box G P E, Jenkins G M, Reinsel G C. Time series analysis: forecasting and control[M]. 3rd ed. New Jersey: Prentice Hall/Pearson Education Asia Ltd, 2005 [5] 李晓白,崔秀伶,郎荣玲.航空发动机性能参数预测方法[J].北京航空航天大学学报, 2008, 34(3): 253-256 Li Xiaobai, Cui Xiuling, Lang Rongling. Forecasting method for aeroengine performance parameters[J]. Journal of Beijing University of Aeronautics and Astronautics, 2008, 34(3): 253-256(in Chinese) [6] Fan J Q, Yao Q W. Nonlinear time series: nonparametric and parametric methods[M]. Berlin: Springer, 2005 [7] Feng Liu,Chai Quek, Geok S N. Neural network model for time series prediction by reinforcement learning // IJCNN2005.Hilton Montreal Bonaventure,Montreal,Canada: IEEE, 2005: 809-814
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