皮肤性病诊疗学杂志2025，Vol.32Issue(11)：791-797,7.DOI:10.3969/j.issn.1674-8468.2025.11.005
SARIMA-GRNN组合模型在江苏省梅毒月度发病数据预测中的应用
The application of a SARIMA-GRNN combination model for predicting monthly syphilis in-cidence in Jiangsu Province
摘要
Abstract
Objective To construct a combination model integrating seasonal autoregressive integrated moving average(SARIMA)and generalized regression neural network(GRNN)to pro-vide a new methodological approach for predicting the incidence trend of syphilis in Jiangsu Prov-ince.Methods Monthly syphilis incidence data from Jiangsu Province from January 2005 to De-cember 2019 were used to establish SARIMA and SARIMA-GRNN combined models,with a com-parative analysis of their fitting accuracy and predictive performance.Results The optimal SARI-MA model parameters were SARIMA(0,1,1)(0,1,1)12,and the best smoothing parameter(spread)for the SARIMA-GRNN combined model was 0.11.In the prediction of syphilis inci-dence in 2019,the root mean square errors(RMSE)for the SARIMA model and the SARIMA-GRNN combination model were 0.336 and 0.287,respectively,while the mean absolute percent-age errors(MAPE)were 10.43％and 8.83％,respectively.And the mean absolute errors(MAE)were 0.278 and 0.245,respectively.Compared to the SARIMA model,the SARIMA-GRNN combination model reduced the RMSE by 14.58％,the MAPE by 15.33％,and the MAE by 11.87％,indicating that the SARIMA-GRNN combination model outperformed the SARIMA model in prediction accuracy.Conclusions The SARIMA-GRNN combination model is more pre-cise than the SARIMA model in fitting monthly syphilis incidence data in Jiangsu Province and has higher prediction accuracy,making it suitable for predicting syphilis incidence in this province.关键词
梅毒/季节性差分自回归移动平均模型/广义回归神经网络/预测Key words
syphilis/seasonal autoregressive integrated moving average model/generalized regression neural network/prediction引用本文复制引用
CHEN Haiyan,ZHOU Luojing..SARIMA-GRNN组合模型在江苏省梅毒月度发病数据预测中的应用[J].皮肤性病诊疗学杂志,2025,32(11):791-797,7.基金项目
扬州市基础研究计划(联合专项)卫生健康类重点项目(2024-1-03) （联合专项）
