基于随机森林算法的企业财务数据风险预警方法

Research on the Risk Early Warning Method of Enterprise Financial Data Based on the Random Forest Algorithm

受到企业财务数据结构变化不确定性因素的影响,传统财务数据风险预警方法存在预警模型误差偏大问题,在实际应用中影响企业对财务风险的预判效果。为了更好地解决这一问题,在随机森林算法下对其风险预警方法展开研究,研究优化共分4部分,分别为基于随机森林算法的企业财务数据风险特征模型构建、基于随机森林算法的财务风险预警指标分析、财务风险数据的时间序列处理以及基于随机森林算法的财务风险预警。仿真测试结果表明。经过随机森林优化后的预警方法。能够在最短的时间内完成财务数据风险信息的精准预警,且调用预警资源最小,具有较高的稳定性、可靠性与推广性。

Influenced by the change of uncertain factors in the structure of enterprise financial data,the traditional financial data risk early warning method has the problem of large error of early warning model,which affects the prediction effect of enterprises on financial risks in practical application.In order to better solve this problem,the risk early warning method is studied under the random forest algorithm.The research optimization is divided into four parts,corresponding to the risk char-acteristic model construction based on random forest algorithm,financial risk early warning index analysis based on random forest algorithm,time series processing of financial risk data and financial risk early warning based on random forest algorithm.The verification of the research results by simulation data shows that the warning method after random forest optimization can complete the accurate warning of financial data risk information in the shortest time,with minimal use of warning resources and high stability,reliability and extension.