【摘要】：目的分析成都地區(qū)臨床月供血量的規(guī)律,以此建立臨床血液月供血量預(yù)測的時(shí)間序列ARIMA模型和乘積季節(jié)ARIMA模型,并動(dòng)態(tài)進(jìn)行模型的分析對比,為血液中心管理工作提供科學(xué)依據(jù)。方法收集2006年至2016年成都市血液中心臨床血液月供血量,建立ARIMA模型和乘積季節(jié)ARIMA模型,預(yù)測2016年10-12月和2017年1-3月臨床血液月供血量。對備選的模型進(jìn)行擬合優(yōu)度的比較,篩選出最優(yōu)的模型,并對模型的相對誤差進(jìn)行評(píng)價(jià)。結(jié)果 ARIMA(0,1,1)模型預(yù)測2016年10-12月和2017年1-3月的相對誤差為1.71%、-7.45%、-3.14%、-7.66%、-15.25%、-9.74%。而ARIMA(0,1,1)×(1,1,1)12模型相對誤差為2.51%、-3.75%、-2.58%、-5.21%、-8.11%、-7.34%。結(jié)論乘積季節(jié)ARIMA模型能夠較好的預(yù)測短期臨床供血量,持續(xù)修正的乘積季節(jié)ARIMA模型能更好的預(yù)測下一季度臨床血液月供血量。
[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.
【作者單位】： 成都市血液中心;西南財(cái)經(jīng)大學(xué);
【基金】：成都市重大基金項(xiàng)目(2013013)
【分類號(hào)】：R197.6
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本文編號(hào)：2240714