Abstract: Considering the new COVID-19 cases are a nonlinear and non-stationary time series, a combined COVID-19 prediction model based on CEEMDAN-HURST algorithm is proposed. Firstly, the time series of newly confirmed cases are decomposed into sub-series with different frequencies using the complete ensemble empirical mode decomposition with adaptive noise（CEEMDAN） algorithm. Secondly, the randomness of each sub-sequence is analyzed by HURST index and the sub-sequence is integrated into three sub-sequences of high frequency, medium frequency and low frequency. The three sub-sequences are predicted by the least square support vector machine（LSSVM）. Finally, the prediction results of each reconstructed subsequence are superimposed to obtain the final predicted value of newly confirmed COVID-19 cases. The results show that the COVID-19 new case combination prediction model based on CEEMDAN-HURST algorithm improves the efficiency and prediction accuracy in the nonlinear time series prediction process. Compared with the CEEMDAN-PE combined model, the mean absolute error and root mean square error are reduced by 11.13% and 29.67%, respectively, indicating that the CEEMDAN-HURST algorithm can effectively solve the problems of low prediction efficiency and low prediction accuracy commonly existed in nonlinear time series forecasting models. Meanwhile, HURST index measures the deviation degree of time series, and the HURST index is introduced to merge, reconstruct and integrate, which can reduce the number of sub-series needed for time series prediction.

Key words: nonlinear non-stationary time series, complete ensemble empirical mode decomposition with adaptive noise（CEEMDAN）, HURST index, combined prediction, COVID-19

摘要： 针对COVID-19新增病例是一个非线性非平稳的时间序列，提出基于CEEMDAN-HURST算法的COVID-19组合预测模型。利用自适应噪声完全集合经验模态分解算法将新增病例时间序列分解为频率不同的子序列；利用HURST指数分析各个子序列的随机性并将子序列整合为高频、中频和低频三种子序列，通过最小二乘支持向量机对这三种子序列分别进行预测；叠加各重构子序列的预测结果，得到COVID-19新增病例的最终预测值。结果表明，基于CEEMDAN-HURST算法的COVID-19新增病例组合预测模型提高了非线性时间序列预测过程中的效率以及预测精度。与CEEMDAN-PE组合模型相比，平均绝对误差、均方根误差分别降低了11.13%和29.67%，表明CEEMDAN-HURST算法可有效解决非线性时间序列预测模型普遍存在的预测效率低和预测精度低的问题；赫斯特（HURST）指数度量了时间序列的偏移程度，引入HURST指数进行合并重构整合，可减少时间序列预测所需要的子序列数目。

关键词: 非线性非平稳时间序列, 自适应噪声的完整集合经验模态分解（CEEMDAN）, HURST指数, 组合预测, COVID-19