[1]马晓梅),史鲁斌),其木格),等.基于ARIMA乘积季节模型和Holt-Winters季节模型的梅毒月发病率预测[J].郑州大学学报(医学版),2018,(01):79-84.[doi:10.13705/j.issn.1671-6825.2017.20.027]
 MA Xiaomei),SHI Lubin),QI Muge),et al.Application of seasonal model of ARIMA and Holt-Winters in prediction of the monthly incidence of syphilis[J].JOURNAL OF ZHENGZHOU UNIVERSITY(MEDICAL SCIENCES),2018,(01):79-84.[doi:10.13705/j.issn.1671-6825.2017.20.027]
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基于ARIMA乘积季节模型和Holt-Winters季节模型的梅毒月发病率预测()
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《郑州大学学报(医学版)》[ISSN:1671-6825/CN:41-1340/R]

卷:
期数:
2018年01期
页码:
79-84
栏目:
应用研究
出版日期:
2018-01-20

文章信息/Info

Title:
Application of seasonal model of ARIMA and Holt-Winters in prediction of the monthly incidence of syphilis
作者:
马晓梅1)史鲁斌2)其木格2)闫国立1)施学忠3)孙春阳1)徐学琴1)赵倩倩1)
1)河南中医药大学公共卫生与预防学科 郑州 450046 2)河南省疾病预防控制中心免疫规划科 郑州 450046 3)郑州大学公共卫生学院卫生统计学教研室 郑州 450001
Author(s):
MA Xiaomei1) SHI Lubin2) QI Muge2) YAN Guoli1) SHI Xuezhong3) SUN Chunyang1) XU Xueqin1) ZHAO Qianqian1)
1)Department of Public Health and Prevention, Henan University of Traditional Chinese Medicine, Zhengzhou 450046 2)Immunization Planning Department, Henan Center for Disease Control and Prevention, Zhengzhou 450046 3)Department of Health Statistics, Colle
关键词:
梅毒 ARIMA Holt-Winters 月发病率
Keywords:
syphilis ARIMA the Holt-Winters the monthly incidence
分类号:
R183
DOI:
10.13705/j.issn.1671-6825.2017.20.027
摘要:
目的:探讨ARIMA乘积季节模型和Holt-Winters季节模型在我国梅毒月发病率预测中的应用价值。方法:以2005年1月至2015年12月梅毒月发病率数据为基础,运用SPSS 22.0和Eviews 8.0分别建立ARIMA乘积季节模型和Holt-Winters季节模型,采用2016年1至6月的实际数据验证模型,评价指标是预测误差和平均绝对误差(MAE)。选择精度较高模型预测2016年7至12月梅毒月发病率。结果:MAE的比较结果表明ARIMA乘积季节模型预测精度优于Holt-Winters季节模型,最优模型是ARIMA(1,1,1)×(0,1,1)12,模型口径为:(1-B)(1-B12)(1+0.374B)xt=(1+0.740B)(1+0.775B12t,2016年7至12月梅毒月发病率的预测结果(1/10万)分别为3.107、2.989、2.879、2.658、2.631、2.644。结论:ARIMA乘积季节模型具有较高的预测精度,可较好地拟合全国梅毒月发病率的演变趋势。
Abstract:
Aim: To explore the application value of ARIMA and Holt-Winters seasonal model for predicting the monthly incidence of syphilis.Methods: SPSS 22.0 and Eviews 8.0 were used to establish the seasonal model of ARIMA and Holt-Winters based on the data of the monthly incidence of syphilis in China from January 2005 to December 2015.Then the actual data from January to June in 2016 were used to confirm the predicted results. The prediction evaluation index was error and MAE. The data from July to December in 2016 were forcasted by the model with higher precision in the similar manner.Results: In the comparison of MAE, the prediction accuracy of the seasonal ARIMA model was higher than the Holt-Winters seasonal model. The optimal model for the monthly incidence was ARIMA(1,1,1)×(0,1,1)12, the model equation was(1-B)(1-B12)(1+0.374B)xt=(1+0.740B)(1+0.775B12t. The predicted results of the monthly incidence of syphilis(1/100 000)from July to December in 2016 were 3.107, 2.989, 2.879, 2.658, 2.631, 2.644.Conclusion: The seasonal ARIMA model features higher predictive accuracy, and could agree well with the trend of the monthly incidence of syphilis.

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相似文献/References:

[1]王小丽),杨永利),施学忠)#,等.几种预测模型对中国梅毒发病率预测效果的比较*[J].郑州大学学报(医学版),2015,(02):164.
 WANG Xiaoli,YANG Yongli,SHI Xuezhong,et al.Efficacy of different prediction models in forecasting incidence rate of syphilis in China[J].JOURNAL OF ZHENGZHOU UNIVERSITY(MEDICAL SCIENCES),2015,(01):164.

备注/Memo

备注/Memo:
【基金项目】国家“十二·五”科技重大专项(2012ZX10004905); 河南省医学科技攻关计划项目(201303003)
【作者简介】闫国立,通信作者,男,1973年2月生,硕士,副教授,研究方向:流行病与卫生统计学,E-mail:13937187109@163.com
更新日期/Last Update: 2018-01-20