The grey model GM(1,1) is widely applied in prediction problems, but the applicable range and the prediction precision of classical GM(1,1) is limited by the background value. In order to improve the applicability of this model, a modified unbiased grey prediction model GM(1,1), which is based on ensemble empirical mode decomposition (EEMD) is put forward. The original data is decomposed into a finite number of intrinsic mode functions (IMFs) by EEMD, and a conversion formula is used to improve the exponential smoothing of every IMF, then using each processed IMF components as input data of Unbiased GM(1,1), we accumulate all results and get the final predict result of the original data. An example is shown that the modified Unbiased GM(1,1) prediction model based on EEMD solved the predicting singular points problem of classical GM(1,1) model and improved the prediction precision.


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    Title :

    A modified unbiased GM(1,1) prediction model based EEMD


    Contributors:
    Jiang, Haixu (author) / Zhang, Ke (author) / Wang, Jingyu (author) / Yang, Tianshe (author)


    Publication date :

    2016-08-01


    Size :

    193417 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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