摘要
目的探讨时序预测模型中的差分自回归滑动平均(ARIMA)和自回归(AR)模型在预测广州市急救调度日出车数量方面的价值。方法采用Matlab仿真软件对广州市2021年1月1日至2021年12月31日的急救调度出车记录分析计算日出车数量时间序列,对该序列进行时序预测模型辨识,得到ARIMA(1,1,1)、AR(4)以及AR(7)模型,利用这些模型对日出车数量做出预测拟合。ARIMA(1,1,1)模型将数据分为训练集和测试集,参数运算采用Prony方法,预测拟合未来的出车数量;AR(4)和AR(7)模型采用均匀系数,预测当天出车数量。结果ARIMA(1,1,1)、AR(4)以及AR(7)都可以实现对日出车数量的有效预测,ARIMA(1,1,1)的预测拟合误差随着预测时间的延长下降。两个月内的急救调度日出车量预测拟合平均绝对百分比误差(MAPE)低于6%,结果基本都位于95%置信区间内,利用模型的残差分析验证了模型显著有效。结论ARIMA模型可以对两个月内的急救调度日出车量做长期预测拟合,AR模型可以对急救调度日出车量做短期有效预测。
Objective To study the value of autoregressive integrated moving average(ARIMA)and autoregressive(AR)models in predicting the daily number of ambulances in prehospital emergency medical services demand in Guangzhou.Methods Matlab simulation software was used to analyze the emergency dispatching departure records in Guangzhou from January 1,2021 to December 31,2021.A time series for the number of ambulances per day was calculated.After identifying the time series prediction model,ARIMA(1,1,1),AR(4)and AR(7)models were obtained.These models were used to predict the number of ambulances per day.ARIMA(1,1,1)model divided the time series into the training set and test set.Prony method was used for parameter calculation,and the demands of number of ambulances of the next few months were forecasted.AR(4)and AR(7)models used uniformity coefficient to forecast the demands of number of ambulances on that very day.Results ARIMA(1,1,1),AR(4)and AR(7)can effectively predict the number of ambulances per day.The prediction fitting error of ARIMA(1,1,1)decreased with the extension of prediction time.The mean absolute percentage error(MAPE)of forecast results of daily vehicle output of emergency dispatching within two months was less than 6%and the predicted results were almost within the 95%confidence interval.The residual analysis of the model verified that the model was significantly effective.Conclusions ARIMA model can make a long-term within two months and effective prediction fitting of the daily vehicle output of emergency dispatching,and AR model can make a short-term and effective prediction of the daily vehicle output of emergency dispatching.
作者
王静
江慧琳
李双明
曾睿
刘佳
李艳玲
朱永城
林建权
陈晓辉
Wang Jing;Jiang Huilin;Li Shuangming;Zeng Rui;Liu Jia;Li Yanling;Zhu Yongcheng;Lin Jianquan;Chen Xiaohui(School of Biomedical Engineering,Guangzhou Medical University,The Second Affiliated Hospital of Guangzhou Medical University,Guangzhou,510260,China;Guangzhou Emergency Medical Command Center,Guangzhou,510000,China)
出处
《中华急诊医学杂志》
CAS
CSCD
北大核心
2022年第8期1153-1158,共6页
Chinese Journal of Emergency Medicine
基金
广州市卫生健康科技重大项目(2020A031005)
广东省医学科学技术研究基金项目(A2022344)
广州市重点学科(2021-2023)。
关键词
差分自回归滑动平均模型
自回归模型
预测
急救调度
MATLAB仿真
Autoregressive Integrated Moving Average model
Autoregressive model
Forecast
Emergency dispatching
Matlab simulation
