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A Novel Optimized Nonlinear Grey Bernoulli Model for Forecasting China’s GDP

机译:一种新型优化非线性灰色Bernoulli预测中国GDP的模型

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摘要

The nonlinear grey Bernoulli model, abbreviated as NGBM(1,1), has been successfully applied to control, prediction, and decision-making fields, especially in the prediction of nonlinear small sample time series. However, there are still some problems in improving the prediction accuracy of NGBM(1,1). In this paper, we propose a novel optimized nonlinear grey Bernoulli model for forecasting Chinaʼs GDP. In the new model, the structure and parameters of NGBM(1,1) are optimized simultaneously. Especially, the latest item of first-order accumulative generating operator (1-AGO) sequence is taken as the initial condition, then background value is reconstructed by optimizing weights of neighbor values in 1-AGO sequence, which is based on minimizing the sum of absolute percentage errors, and finally, we establish the new model based on the rolling mechanism. Prediction accuracy of the proposed model is investigated through some simulations and a real example application, and the proposed model is applied to forecast the annual GDP in China from 2019 to 2023.
机译:非线性灰色Bernoulli模型缩写为NGBM(1,1),已成功应用于控制,预测和决策场,尤其是在非线性小型采样时间序列的预测中。然而,在提高NGBM的预测精度(1,1)方面仍存在一些问题。本文提出了一种新颖的非线性非线性灰色Bernoulli模型,用于预测中国GDP。在新模型中,NGBM(1,1)的结构和参数同时进行优化。特别是,将首次累计生成运算符(1-前)序列的最新项目作为初始条件,然后通过在1年度序列中优化邻居值的权重来重建背景值,这是基于最小化的总和绝对百分比误差,最后,我们基于滚动机制建立了新模型。通过一些模拟和实际示例应用研究了所提出的模型的预测准确性,并拟议的模型从2019年到2023年预测中国的年度GDP。

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