自回归求和移动平均模型对临床供血量的分析预测

发布时间：2018-09-13 09:12

【摘要】：目的分析成都地区临床月供血量的规律,以此建立临床血液月供血量预测的时间序列ARIMA模型和乘积季节ARIMA模型,并动态进行模型的分析对比,为血液中心管理工作提供科学依据。方法收集2006年至2016年成都市血液中心临床血液月供血量,建立ARIMA模型和乘积季节ARIMA模型,预测2016年10-12月和2017年1-3月临床血液月供血量。对备选的模型进行拟合优度的比较,筛选出最优的模型,并对模型的相对误差进行评价。结果 ARIMA(0,1,1)模型预测2016年10-12月和2017年1-3月的相对误差为1.71%、-7.45%、-3.14%、-7.66%、-15.25%、-9.74%。而ARIMA(0,1,1)×(1,1,1)12模型相对误差为2.51%、-3.75%、-2.58%、-5.21%、-8.11%、-7.34%。结论乘积季节ARIMA模型能够较好的预测短期临床供血量,持续修正的乘积季节ARIMA模型能更好的预测下一季度临床血液月供血量。
[Abstract]:Objective to analyze the regularity of clinical monthly blood supply in Chengdu area, and to establish a time series ARIMA model and a product seasonal ARIMA model for predicting monthly clinical blood supply, and to analyze and compare the models dynamically. To provide scientific basis for the management of blood center. Methods the monthly clinical blood supply of Chengdu Blood Center from 2006 to 2016 was collected, the ARIMA model and the seasonal ARIMA model were established to predict the monthly clinical blood supply from October to December 2016 and January to March 2017. The optimum model is screened by comparing the fit degree of the alternative model and the relative error of the model is evaluated. Results the ARIMA model predicted that the relative error between October and December 2016 and from January to March 2017 was 1.71 and 7.45, and 3.14 and 7.66, respectively, and between 15.25 and 9.74. And the relative error of ARIMA (0) 脳 (1) 脳 (1) 1) 12 model is 2.51% and 2.51% -2.58% -5.21% and 8.11% and 7.34%, respectively. Conclusion the seasonal ARIMA model can better predict the short-term clinical blood supply, and the continuously modified seasonal ARIMA model can better predict the monthly blood supply in the next quarter.
【作者单位】： 成都市血液中心;西南财经大学;
【基金】：成都市重大基金项目(2013013)
【分类号】：R197.6
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本文编号：2240714