自回归移动平均混合模型在中国道路交通伤害预测中的应用

庞媛媛; 张徐军; 涂志斌; 崔梦晶; 顾月

Abstract

庞媛媛,张徐军,涂志斌,崔梦晶,顾月.自回归移动平均混合模型在中国道路交通伤害预测中的应用[J].Chinese journal of Epidemiology,2013,34(7):736-739

自回归移动平均混合模型在中国道路交通伤害预测中的应用

Autoregressive integrated moving average model in predicting road traffic injury in China

Received:January 21, 2013

DOI：10.3760/cma.j.issn.0254-6450.2013.07.018

KeyWord: 道路交通伤害时间序列分析自回归移动平均混合模型预测

English Key Word: Road traffic injury Time series analysis Autoregressive integrated moving average model Forecasting

FundProject:

Author Name	Affiliation	E-mail
Yuan yuan	Injury Prevention Research Institute School of Public Health.Southeast University.Nanjing 210009.China
ZHANG Xu-jun	Injury Prevention Research Institute School of Public Health.Southeast University.Nanjing 210009.China	xjzhang@seu.edu.cn
TU Zhi-bin	Injury Prevention Research Institute School of Public Health.Southeast University.Nanjing 210009.China
CUI Meng-jing	Injury Prevention Research Institute School of Public Health.Southeast University.Nanjing 210009.China
GU yue	Injury Prevention Research Institute School of Public Health.Southeast University.Nanjing 210009.China

Hits: 2989

Download times: 1706

Abstract:

探讨时间序列分析的自回归移动平均混合模型(ARIMA)在中国道路交通伤害(RTI)预测中的应用。收集1951-2011年中国道路交通伤害资料, 进行时间序列分析, 建立ARIMA模型。构建得到RTI事故起数ARIMA(1, 1, 0)预测模型为Yt=eY+0.456Y+e, 其中, et为随机误差, 模型残差序列为白噪声, Ljung.Box检验P>O.05, 统计量无统计学意义, 拟合效果良好。应用该模型预测2011年中国RTI事故起数, 预测值与实际观测结果相符, 实际观测值在预测值95%CI内。用该模型预测2012年中国RTI事故起数, 预测值(95%c, )为207838(107579～401536)。应用ARIMA模型能较好地预测中国道路交通伤害情况。

English Abstract:

This research aimed to explore the application of autoregressive integrated moving average(ARIMA)model of time series analysis in predicting road traffic injury(RTI)in China and to provide scientific evidence for the prevention and control of RTI.Database was created based on the data collected from monitoring sites in China from 1951 t0 2011.The ARIMA model was made.Then it was used to predict RTI in 2012.The ARIMA model of the RTI cases was Yt=eY+o 456、Y+e-¹htht(estands for random error).The residual error with 16 lags was white noise and the Ljung-Box test statistic for the model was no statistical significance.The model fired the data well.True value of RTI cases in 20 11 was within 95%CI of predicted values obtained from present model.The model was used to predict value of RTI cases in 2012, and the predictor(95%CI)was 207838(107 579-40 1 536).The ARIMAmodel could fit the trend of IHI ill China．

View Fulltext Html FullText View/Add Comment Download reader