摘要： 针对门诊量波动幅度较大的时间序列预测问题，先采用经验模态分解(EMD)将非线性较强的原始数据进行分解，然后通过极限学习机(ELM)将分解后的各个序列分量进行建模，最后将各个分量的预测值相加得出最终结果。将BP神经网络、ELM两个单一模型与EMDELM组合模型进行对比验证，实验结果表明组合模型的精准度明显好于两个单一模型。

中图分类号：TP391
文献标识码：A
DOI:10.19358/j.issn.2097-1788.2023.06.016
引用格式：樊冲.基于EMD和ELM相结合的门诊量预测模型研究［J］.网络安全与数据治理，2023，42（6）：97-102.

Research on outpatient volume prediction model based on the combination of EMD and ELM

Abstract： Aiming at the time series prediction with largefluctuations of outpatient volume, firstly, it is necessary to decompose original data with strong nonlinearity by Empirical Mode Decomposition (EMD), model these decomposed sequence components by Extreme Learning Machine (ELM), and then sum up the prediction volume of these sequence components and finally draw a conclusion. The single models of BP neural network and ELM were compared and verified with the combined model of EMDELM, and it was found that the accuracy of the combined model was significantly better than that of the single models according to the experimental outcomes.

Key words : prediction model; time series; prediction of outpatient volume; Extreme Learning Machine(ELM); Empirical Mode Decomposition(EMD)