Li Guohui, Lu Jin, Chen Kang, Yang Hong
School of Electronic Engineering, Xi'an University of Posts and Telecommunications, Xi'an, Shaanxi 710121, China.
Eng Appl Artif Intell. 2023 Jun 26:106692. doi: 10.1016/j.engappai.2023.106692.
With the emergence of new mutant corona virus disease 2019 (COVID-19) strains such as Delta and Omicron, the number of infected people in various countries has reached a new high. Accurate prediction of the number of infected people is of far-reaching sig Nificance to epidemiological prevention in all countries of the world. In order to improve the prediction accuracy of COVID-19 daily new case data, a new hybrid prediction model of COVID-19 is proposed, which consists of four modules: decomposition, complexity judgment, prediction and error correction. Firstly, singular spectrum decomposition is used to decompose the COVID-19 data into singular spectrum components (SSC). Secondly, the complexity judgment is innovatively divided into high-complexity SSC and low-complexity SSC by neural network estimation time entropy. Thirdly, an improved LSSVM by GODLIKE optimization algorithm, named GLSSVM, is proposed to improve its prediction accuracy. Then, each low-complexity SSC is predicted by ARIMA, and each high-complexity SSC is predicted by GLSSVM, and the prediction error of each high-complexity SSC is predicted by GLSSVM. Finally, the predicted results are combined and reconstructed. Simulation experiments in Japan, Germany and Russia show that the proposed model has the highest prediction accuracy and the lowest prediction error. Diebold Mariano (DM) test is introduced to evaluate the model comprehensively. Taking Japan as an example, compared with ARIMA prediction model, the RMSE, average error and MAPE of the proposed model are reduced by 93.17%, 91.42% and 81.20% respectively.
随着德尔塔和奥密克戎等新型新冠病毒疾病2019（COVID-19）毒株的出现，各国的感染人数达到了新高。准确预测感染人数对世界各国的流行病学预防具有深远意义。为了提高COVID-19每日新增病例数据的预测准确性，提出了一种新的COVID-19混合预测模型，该模型由分解、复杂度判断、预测和误差校正四个模块组成。首先，使用奇异谱分解将COVID-19数据分解为奇异谱分量（SSC）。其次，通过神经网络估计时间熵将复杂度判断创新性地分为高复杂度SSC和低复杂度SSC。第三，提出了一种基于GODLIKE优化算法的改进最小二乘支持向量机（LSSVM），即GLSSVM，以提高其预测准确性。然后，用自回归积分滑动平均模型（ARIMA）预测每个低复杂度SSC，用GLSSVM预测每个高复杂度SSC，并用GLSSVM预测每个高复杂度SSC的预测误差。最后，将预测结果进行组合和重构。在日本、德国和俄罗斯进行的仿真实验表明，所提模型具有最高的预测准确性和最低的预测误差。引入迪氏检验（DM检验）对模型进行综合评估。以日本为例，与ARIMA预测模型相比，所提模型的均方根误差（RMSE）、平均误差和平均绝对百分比误差（MAPE）分别降低了93.17%、91.42%和81.20%。
