Application of Grey Model and Neural Network in Financial Revenue Forecast

作     者：Yifu Sheng Jianjun Zhang Wenwu Tan Jiang Wu Haijun Lin Guang Sun Peng Guo 

作者机构：College of Engineering and DesignHunan Normal UniversityChangsha410081China Big Data InstituteHunan University of Finance and EconomicsChangsha410205China University Malaysia SabahSabah88400Malaysia 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2021年第69卷第12期

页      面：4043-4059页

核心收录：

学科分类：0202[经济学-应用经济学] 02[经济学] 020205[经济学-产业经济学] 

基　　金：This research was funded by the National Natural Science Foundation of China(No.61304208) Scientific Research Fund of Hunan Province Education Department(18C0003) Research project on teaching reform in colleges and universities of Hunan Province Education Department(20190147) Changsha City Science and Technology Plan Program(K1501013-11) Hunan Normal University University-Industry Cooperation.This work is implemented at the 2011 Collaborative Innovation Center for Development and Utilization of Finance and Economics Big Data Property,Universities of Hunan Province,Open project,grant number 20181901CRP04 

主　　题：Fiscal revenue lasso regression gray prediction model BP neural network 

摘      要：There are many influencing factors of fiscal revenue,and traditional forecasting methods cannot handle the feature dimensions well,which leads to serious over-fitting of the forecast results and unable to make a good estimate of the true future *** grey neural network model fused with Lasso regression is a comprehensive prediction model that combines the grey prediction model and the BP neural network model after dimensionality reduction using *** can reduce the dimensionality of the original data,make separate predictions for each explanatory variable,and then use neural networks to make multivariate predictions,thereby making up for the shortcomings of traditional methods of insufficient prediction *** this paper,we took the financial revenue data of China’s Hunan Province from 2005 to 2019 as the object of ***,we used Lasso regression to reduce the dimensionality of the *** the grey prediction model has the excellent predictive performance for small data volumes,then we chose the grey prediction model to obtain the predicted values of all explanatory variables in 2020,2021 by using the data of 2005–***,considering that fiscal revenue is affected by many factors,we applied the BP neural network,which has a good effect on multiple inputs,to make the final forecast of fiscal *** experimental results show that the combined model has a good effect in financial revenue forecasting.