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

LIU Jin-lian; et al

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

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- Applications of multiple seasonal ARlMA model on predictive workload of inpatient department
- Journal of Chongqing Medical University Issue 4, Pages: 475-477(2008)
- 作者机构：
  
  1. 重庆医科大学公共卫生学院卫生统计教研室
  2. 重庆医科大学公共卫生学院卫生统计教研室,重庆,400016
- 作者简介：
- 基金信息：
- DOI：
  CLC： R197.3
- Published：2008，
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[1]刘金莲,钟晓妮.ARIMA季节乘积模型在预测医院月住院量的应用[J].重庆医科大学学报,2008(04):475-477.

LIU Jin-lian, et al. Applications of multiple seasonal ARlMA model on predictive workload of inpatient department. [J]. Journal of Chongqing Medical University (4):475-477(2008)
[1]刘金莲,钟晓妮.ARIMA季节乘积模型在预测医院月住院量的应用[J].重庆医科大学学报,2008(04):475-477. DOI：

LIU Jin-lian, et al. Applications of multiple seasonal ARlMA model on predictive workload of inpatient department. [J]. Journal of Chongqing Medical University (4):475-477(2008) DOI：

摘要

目的:探讨ARIMA季节乘积模型在时间序列资料分析中的应用

建立住院量的预测模型。方法:利用重庆市某医院2002～2005年的月住院量

采用条件最小二乘法估计模型参数

按照残差不相关原则、简洁原则确定模型结构

依据AIC和SBC准则确定模型阶数

建立ARIMA预测模型。结果:模型参数有统计学意义。方差估计值为0.003 175

AIC=-94.245 1

SBC=-91.252 1。对模型进行白噪声残差分析

拟合优度结果表明ARIMA的最优模型为:（1-B）（1-B12）Zt（=1-0.543 02B）（1-B12）at。结论:用所建立模型对月住院量进行预测

结果表明ARIMA是一种短期预测精度较高的预测模型。

Abstract

Objective:To discuss the application of multiple seasonal ARlMA model to data in time series and fit predictive model on workload of inpatient department.Methods:Parameter of model is estimated using conditional least squares method according to the data of workload of inpatient department in a hospital in Chongqing from 2002 to 2005.The structure is determined according to criteria of residual un-correlation and concision.ARIMA predictive model was fitted and the order of model was confirmed through Akaike’s Information Criterion and Schwarz Bayesian Criterion.Results:Statistics assisted estimation of the model.The estimation of variance is 0.003 175

AIC=-94.245 1

SBC=-91.252 1.Using analysis of white-noise residual of mode1

fit of rich table show that the best estimated ARIMA model is(1-B)(1-B12)Zt=(1-0.543 02B)(1-B12)at.Conclusion:The model of ARIMA can be used to forecast for workload of inpatient department with high prediction precision of short-term time series.

关键词

时间序列预测自回归求和移动平均模型

Keywords

time seriespredictionARIMA model

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