The predictive value of time series forecasting model in prehospital emergency medical services demand in Guangzhou
10.3760/cma.j.issn.1671-0282.2022.08.028
- VernacularTitle:时序预测模型对广州市急救需求量的预测价值
- Author:
Jing WANG
1
;
Huilin JIANG
;
Shuangming LI
;
Rui ZENG
;
Jia LIU
;
Yanling LI
;
Yongcheng ZHU
;
Jianquan LIN
;
Xiaohui CHEN
Author Information
1. 广州医科大学附属第二医院急诊科，广州医科大学生物医学工程学院，广州 510260
- Keywords:
Autoregressive Integrated Moving Average model;
Autoregressive model;
Forecast;
Emergency dispatching;
Matlab simulation
- From:
Chinese Journal of Emergency Medicine
2022;31(8):1153-1158
- CountryChina
- Language:Chinese
-
Abstract:
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.
