论文标题
滚动优化的非线性灰色bernoulli型号RONGBM（1,1），并在预测COVID-19受感染的情况下应用
A Rolling Optimized Nonlinear Grey Bernoulli Model RONGBM(1,1) and application in predicting total COVID-19 infected cases
论文作者
论文摘要
非线性灰色Bernoulli模型NGBM（1，1）是一个最近开发的灰色模型，它在不同字段中具有各种应用，这主要是由于其在处理具有非线性变化的小型时间序列数据集方面的准确性。在本文中，为了充分提高该模型的准确性，提出了一种新型模型，即滚动优化的非线性灰色Bernoulli模型RONGBM（1，1）。该模型将滚动机制与所有模型参数（指数，背景值和初始条件）的同时优化结合在一起。通过预测2013年至2018年的越南GDP的预测，该新模型的准确性已得到了明显的证明，然后将其应用于预测全球范围内的COVID-19受感染总案例。
The Nonlinear Grey Bernoulli Model NGBM(1, 1) is a recently developed grey model which has various applications in different fields, mainly due to its accuracy in handling small time-series datasets with nonlinear variations. In this paper, to fully improve the accuracy of this model, a novel model is proposed, namely Rolling Optimized Nonlinear Grey Bernoulli Model RONGBM(1, 1). This model combines the rolling mechanism with the simultaneous optimization of all model parameters (exponential, background value and initial condition). The accuracy of this new model has significantly been proven through forecasting Vietnam's GDP from 2013 to 2018, before it is applied to predict the total COVID-19 infected cases globally by day.
