Comparison of the effects of three time series models in predicting the trend of erythrocyte blood demand
10.13303/j.cjbt.issn.1004-549x.2025.02.016
- VernacularTitle:三种时间序列模型预测红细胞类血液需求趋势效果对比
- Author:
Yajuan QIU
1
;
Jianping ZHANG
2
;
Jia LUO
1
;
Peilin LI
2
;
Mengzhuo LUO
1
;
Qiongying LI
1
;
Ge LIU
1
;
Qing LEI
1
;
Kai LIAO
1
Author Information
1. Changsha Blood Center, Changsha 410024, China
2. Computational Science Department of Mathematics, Xiangtan University, Xiangtan 411100, China
- Publication Type:Journal Article
- Keywords:
ARIMA model;
LSTM model;
ARIMA-LSTM model;
demand/prediction;
erythrocyte
- From:
Chinese Journal of Blood Transfusion
2025;38(2):257-262
- CountryChina
- Language:Chinese
-
Abstract:
[Objective] To analyse and predict the tendencies of using erythrocyte blood in Changsha based on the autoregressive integrated moving average (ARIMA) model, long short-term memory (LSTM) and ARIMA-LSTM combination model, so as to provide reliable basis for designing a feasible and effective blood inventory management strategy. [Methods] The data of erythrocyte usage from hospitals in Changsha between January 2012 and December 2023 were collected, and ARIMA model, LSTM model and ARIMA-LSTM combination model were established. The actual erythrocyte consumption from January to May 2024 were used to assess and verify the prediction effect of the models. The extrapolation prediction accuracy of the models were tested using two evaluation indicators: mean absolute percentage error (MAPE) and root mean square error (RMSE), and then the prediction performance of the model was compared. [Results] The RMSE of LSTM model, optimal model ARIMA(1,1,1)(1,1,1)12 and ARIMA-LSTM combination model were respectively 5 206.66, 3 096.43 and 2 745.75, and the MAPE were 18.78%,11.54% and 9.76% respectively, which indicated that the ARIMA-LSTM combination model was more accurate than the ARIMA model and LSTM model, and the prediction results was basically consistent with the actual situation. [Conclusion] The ARIMA-LSTM model can better predict the clinical erythrocyte consumption in Changsha in the short term.
