2020/08/13 by Hoang‐Anh Ngo, Hoang Anh Ngo, Ngo, Hoang Anh +3
Decision Sciences · Engineering · Mathematics · #62M10 (Primary) 62P10 (Secondary) #Applications (stat.AP) #Energy Load and Power Forecasting #FOS: Computer and information sciences #FOS: Electrical engineering #Grey System Theory Applications #Methodology (stat.ME) #Signal Processing (eess.SP) #eess.SP #electronic engineering #information engineering #msc:62M10 #msc:62P10 #stat.AP #stat.ME
paper · pdf · doi:10.48550/arxiv.2008.07581
Accepted paper at the 2020 International Congress of Grey Systems and Uncertainty Analysis (GSUA)
arxiv created 2020/08/13 · openalex publication_date 2020/08/13 · arxiv updated 2020/08/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.