Application of autoregressive integrated moving average model（ARIMA） in the prediction of Japanese encephalitis in Chongqing

Zhou Chunbei; Yao Ning

doi:10.13406/j.cnki.cyxb.001372

您当前的位置：

首页 >

文章列表页 >

Application of autoregressive integrated moving average model（ARIMA） in the prediction of Japanese encephalitis in Chongqing

更新时间：2024-01-17

- Application of autoregressive integrated moving average model（ARIMA） in the prediction of Japanese encephalitis in Chongqing
- Journal of Chongqing Medical University Vol. 42, Issue 10, Pages: 1345-1349(2017)
- 作者机构：
  
  重庆市疾病预防控制中心
- 作者简介：
- 基金信息：
- DOI：10.13406/j.cnki.cyxb.001372
  CLC： R512.32
- Published：2017，
扫描看全文
Zhou Chunbei, Yao Ning. Application of autoregressive integrated moving average model（ARIMA） in the prediction of Japanese encephalitis in Chongqing. [J]. Journal of Chongqing Medical University 42(10):1345-1349(2017)
DOI：

Zhou Chunbei, Yao Ning. Application of autoregressive integrated moving average model（ARIMA） in the prediction of Japanese encephalitis in Chongqing. [J]. Journal of Chongqing Medical University 42(10):1345-1349(2017) DOI： 10.13406/j.cnki.cyxb.001372.

摘要

目的:应用自回归求和移动平均(autoregressive integrated moving average

ARIMA)乘积季节模型预测重庆市流行性乙型脑炎(简称乙脑)发病数。方法:利用R软件对重庆市2006年1月到2015年6月乙脑报告病例数进行ARIMA模型建模拟合

选择预测模型进行相互比较确定最优模型。用2015年7至12月实际报告病例数与拟合值的比较来评价模型的预测效果

并对2016至2017年重庆市乙脑报告发病数进行预测。结果:重庆市乙脑发病人数呈逐年下降趋势

报告病例具有明显季节分布特征

ARIMA(0

1)×(1

2)12模型很好地拟合了时间序列

该模型赤池信息量准则(Akaike information criterion

AIC)、许瓦兹贝叶斯准则(Schwarz Bayesian criterion

SBC)值均最小且预测值与实际值的平均相对误差为0.12

平均绝对百分比误差为7.81%。进一步用该模型预测重庆市2016至2017年乙脑病例数分别为35例和32例

发病高峰仍是7至8月。结论:利用ARIMA乘积季节模型对乙脑发病数拟合较好

短期预测结果良好;与2015年比较

预测2016至2017年乙脑报告发病数略微减少。

Abstract

Objective:To predict the possible Japanese encephalitis（JE）cases in Chongqing using autoregressive integrated moving average model（ARIMA）. Methods:An ARIMA prediction model was established by R software using data of reported JE cases in Chongqing from January 2006 to June 2015. Comparison between predicted numbers and reported cases from July to December of2015 was examined for model assessment

then the most fitted model was used to predict JE cases in year 2016-2017. Results:Reported JE cases in Chongqing decreased annually

and fit the characteristic of seasonal change. ARIMA（0

1）×（1

2）12can appropriately fit the time series with the smallest value of Akaike information criterion（AIC）and Schwarz Bayesian criterion（SBC）index with a mean relative residual of 0.12

the mean absolute percentage error（MAPE）was 7.81%. The predicted JE cases in year2016 and 2017 were 35 and 32 respectively

the incidence peak was still at July and August. Conclusion:The ARIMA model can be used to fit the time series trend of JE disease

predicted JE cases in year 2016 and 2017 would be less than that of reported cases in2015.

关键词

流行性乙型脑炎自回归求和移动平均乘积季节模型预测

Keywords

Japanese encephalitismultiple seasonal ARIMA modelprediction

references

Views

117

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Prediction of influenza in Chongqing，China，based on the Autoregressive Integrated Moving Average model

Predictive value of NLR and PLR for outcome after immunotherapy in advanced urothelial carcinoma

Prediction of endoscopic disease activity by analyzing blood biochemical results in children with Crohn's disease

Value of carbohydrate antigen 125, human chorionic gonadotropin and progesterone in predicting pregnancy outcome in patients with threatened abortion at early stage

Applications of multiple seasonal ARlMA model on predictive workload of inpatient department

Related Author

Zou Xiaojiang

Zhao Han

Wang Qiyin

Ye Mengliang

Sun Wei

Wei Xiaohang

Jiang Qing

Chen Yong

Related Institution

School of Public Health，Chongqing Medical University

Department of Urology,The Second Affiliated Hospital of Chongqing Medical University

Department of Urology,Chongqing University Fuling Hospital

Department of Infectious Gastroenterology, Chengdu Women's and Children's Central Hospital

Department of Gastroenterology, Children's Hospital of Chongqing Medical University, Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing Key Laboratory of Pediatrics

⁰