Li Guohui, Chen Kang, Yang Hong
School of Electronic Engineering, Xi'an University of Posts and Telecommunications, Xi'an, Shaanxi 710121, China.
Process Saf Environ Prot. 2022 Jan;157:1-19. doi: 10.1016/j.psep.2021.10.047. Epub 2021 Nov 2.
Establishing an accurate and efficient prediction model is of great significance for governments and other social organizations to formulate prevention and control policies and curb the explosive spread of the pandemic. To improve prediction accuracy of cumulative COVID-19 confirmed data, a new hybrid prediction model based on gradient-based optimizer variational mode decomposition (GVMD), extreme learning machine (ELM), and autoregressive integrated moving average (ARIMA), named GVMD-ELM-ARIMA, is proposed. To solve the problem of selecting the value and the penalty factor in variational mode decomposition (VMD), this paper proposes gradient-based optimizer variational mode decomposition (GVMD), which realizes the self-adaptive determination of value and value. Firstly, GVMD decomposes the cumulative COVID-19 confirmed data into some intrinsic mode functions (IMFs) and a residual component (IMFr). Secondly, IMFs are predicted by ELM. Then, IMFr is predicted by ARIMA. Finally, the final prediction results are obtained by reconstructing the prediction result of IMFs and IMFr. The cumulative COVID-19 confirmed data of the United States, India and Russia is used to verify its effectiveness. Taking the United States as an example, compared with the average MAPE, RMSE and MAE of the single model, the average MAPE of the hybrid model is reduced by 47.27%, the average RMSE is reduced by 44.50%, and the average MAE is reduced by 55.34%. Compared with GVMD-ELM-ELM, GVMD-ELM-ARIMA proposed in this paper reduces the MAPE by 60%, the RMSE by 56.85%, and the MAE by 61.61%. The experimental results show that GVMD-ELM-ARIMA has best prediction accuracy, and it provides a new method for predicting the cumulative COVID-19 confirmed data.
建立准确、高效的预测模型对于政府和其他社会组织制定防控政策、遏制疫情的爆发式传播具有重要意义。为提高新冠肺炎累计确诊数据的预测精度，提出了一种基于梯度优化变分模态分解（GVMD）、极限学习机（ELM）和自回归积分滑动平均（ARIMA）的新型混合预测模型，即GVMD-ELM-ARIMA。为解决变分模态分解（VMD）中参数 和惩罚因子 的选择问题，本文提出了梯度优化变分模态分解（GVMD），实现了参数 和参数 的自适应确定。首先，GVMD将新冠肺炎累计确诊数据分解为若干个本征模态函数（IMF）和一个残余分量（IMFr）。其次，利用ELM对IMF进行预测。然后，用ARIMA对IMFr进行预测。最后，通过重构IMF和IMFr的预测结果得到最终预测结果。利用美国、印度和俄罗斯的新冠肺炎累计确诊数据验证了其有效性。以美国为例，与单一模型的平均平均绝对百分比误差（MAPE）、均方根误差（RMSE）和平均绝对误差（MAE）相比，混合模型的平均MAPE降低了47.27%，平均RMSE降低了44.50%，平均MAE降低了55.34%。与GVMD-ELM-ELM相比，本文提出的GVMD-ELM-ARIMA的MAPE降低了60%，RMSE降低了56.85%，MAE降低了61.61%。实验结果表明，GVMD-ELM-ARIMA具有最佳的预测精度，为新冠肺炎累计确诊数据的预测提供了一种新方法。
