摘要
为了实现更加稳健和精准的门诊量预测,构建了一种基于SARIMA-LSTM的门诊量预测模型。该方法首先使用SARIMA模型对门诊量进行单指标建模,提取门诊量指标蕴含的周期、趋势等信息,然后构建了以节日天数、法定上班天数、平均最高气温等多个相关指标为输入的多对一LSTM模型,对SARIMA模型残差进行进一步学习,实现残差与多个变量间的非线性关系抽取。实证结果表明,构建SARIMA-LSTM混合模型相较5种主流预测方法具有更高的一步预测精度,具有较好的实际应用价值。
In order to achieve more robust and accurate outpatient volume prediction,a hybrid prediction model based on SARIMA-LSTM was constructed.SARIMA model was used to build a single index model of outpatient volume to extract the cycle,trend and other information contained in outpatient volume index.Then multiple related indexes,including holiday days,legal working days,average maximum temperature,were used as input of a many-to-one LSTM model,in order to further learn the residual of SARIMA model and extract the nonlinear relationship between residual and multiple variables.The empirical results show that the SARIMA-LSTM hybrid model constructed in this paper has higher prediction accuracy than the five mainstream prediction methods,so it has good practical application value.
作者
卢鹏飞
须成杰
张敬谊
韩侣
李静
LU Pengfei;XU Chengjie;ZHANG Jingyi;HAN Lyu;LI Jing(Wonders Information Co.,Ltd.,Shanghai 201112,China;Gynecology Hospital of Fudan University,Shanghai 200090,China;Changchun University of Science and Technology,Changchun 130022,China)
出处
《大数据》
2019年第6期101-110,共10页
Big Data Research
基金
上海市科委民生科技支撑计划专项临床医学科技创新项目(No.17411950500,No.17411950505)~~
关键词
季节性差分自回归滑动平均模型
长短期记忆网络
门诊预测
残差
seasonal auto-regressive integrated moving average model
long short term memory
outpatient forecast
residual
