Open Access

ARTICLE

Application of Grey Model and Neural Network in Financial Revenue Forecast

Yifu Sheng¹, Jianjun Zhang^1,*, Wenwu Tan¹, Jiang Wu¹, Haijun Lin¹, Guang Sun², Peng Guo³

1 College of Engineering and Design, Hunan Normal University, Changsha, 410081, China
2 Big Data Institute, Hunan University of Finance and Economics, Changsha, 410205, China
3 University Malaysia Sabah, Sabah, 88400, Malaysia

* Corresponding Author: Jianjun Zhang. Email:

Computers, Materials & Continua 2021, 69(3), 4043-4059. https://doi.org/10.32604/cmc.2021.019900

Received 30 April 2021; Accepted 12 June 2021; Issue published 24 August 2021

Abstract

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 trend. The 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 Lasso. It 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 accuracy. In this paper, we took the financial revenue data of China’s Hunan Province from 2005 to 2019 as the object of analysis. Firstly, we used Lasso regression to reduce the dimensionality of the data. Because 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 2005–2019. Finally, 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 revenue. The experimental results show that the combined model has a good effect in financial revenue forecasting.

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Keywords

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