Online public opinion prediction based on a novel seasonal grey decomposition and ensemble model
作者：
Highlights：
• A novel hybrid grey seasonal model is proposed to predict online public opinion.
• Based on the grey differential information principle, MEGM(1,1) model is proposed.
• The dynamic seasonal factors that extracted from seasonal sequence are proposed.
• The nonlinear Bernoulli equation is introduced to the establishment of SMEGBM model.
• Comparative studies illustrate the effectiveness of the SMEGBM-ARIMA model.
摘要
•A novel hybrid grey seasonal model is proposed to predict online public opinion.•Based on the grey differential information principle, MEGM(1,1) model is proposed.•The dynamic seasonal factors that extracted from seasonal sequence are proposed.•The nonlinear Bernoulli equation is introduced to the establishment of SMEGBM model.•Comparative studies illustrate the effectiveness of the SMEGBM-ARIMA model.
论文关键词：Prediction,Online public opinion,Grey models,Seasonal fluctuation,Decomposition and ensemble
论文评审过程：Received 23 May 2022, Revised 20 July 2022, Accepted 30 July 2022, Available online 5 August 2022, Version of Record 17 August 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.118341
