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An Improved Nonlinear Grey Bernoulli Model Combined with Fourier Series

机译：改进的结合傅里叶级数的非线性灰色伯努利模型

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摘要

Grey forecasting is a dynamic forecasting model and has been widely used in various fields. In recent years, many scholars have proposed new procedures or new models to improve the precision accuracy of grey forecasting for the fluctuating data sets. However, the prediction accuracy of the grey forecasting models existing may not be always satisfactory in different scenario. For example, the data are highly fluctuating are with lots of noise. In order to deal with this issue, a Fourier Nonlinear Grey Bernoulli Model (1, 1) (abbreviated as F-NGBM (1, 1)) is proposed to enhance the forecasting performance. The proposed model was established by using Fourier series to modify the residual errors of Nonlinear Grey Bernoulli Model (1, 1) (abbreviated as (NGBM (1, 1)). To verify the effectiveness of the proposed model, fluctuation data of the numerical example in Wang et al.'s paper (Wang et al. 2011) and practical application are used. Both of these simulation results demonstrate that the proposed model could forecast more precisely than several different kinds of grey forecasting models. For future direction, this proposed model can be applied to forecast the performance with the high fluctuation data in the different industries.
机译：灰色预测是一种动态预测模型，已广泛应用于各个领域。近年来，许多学者提出了新的程序或新模型，以提高波动数据集的灰色预测的精度。但是，现有的灰色预测模型的预测精度可能在不同情况下并不总是令人满意的。例如，数据波动很大，并且噪声很大。为了解决这个问题，提出了一种傅立叶非线性灰色伯努利模型（1，1）（简称为F-NGBM（1，1））来提高预测性能。利用傅立叶级数修正非线性灰色伯努利模型（1，1）（简称（NGBM（1，1））的残差，建立了该模型，验证了该模型的有效性。本文使用了Wang等人的论文（Wang等人，2011）中的例子和实际应用，这两个仿真结果均表明，所提出的模型比几种不同的灰色预测模型可以更精确地进行预测。所提出的模型可用于预测具有高波动性数据的不同行业的绩效。

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  • 来源
    《Mathematical Problems in Engineering》 |2015年第19期|740272.1-740272.7|共7页
  • 作者

    Wang Chia-Nan; Phan Van-Thanh;

  • 作者单位

    Natl Kaohsiung Univ Appl Sci, Dept Ind Engn & Management, Kaohsiung 807, Taiwan;

    Natl Kaohsiung Univ Appl Sci, Dept Ind Engn & Management, Kaohsiung 807, Taiwan;

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