SARIMA-RBF组合模型在流行性腮腺炎流行趋势预测中的应用

doi:10.12183/j.scjpm.2020.0214

摘要/Abstract

摘要： 目的探讨SARIMA-RBF组合模型拟合及预测我国流行性腮腺炎（流腮）流行趋势的应用。方法利用全国2004—2015年流腮逐月发病率建立SARIMA模型。将基于SARIMA模型的拟合值作为输入向量,实际值作为输出向量,根据时间因素作为输入向量与否建立2个SARIMA-RBF组合模型（加入时间因素记为组合模型A,不加入时间因素记为组合模型B）。运用SARIMA模型和2个SARIMA-RBF组合模型预测2016年7—12月流腮发病率并与实际值比较,采用平均绝对百分比误差（MAPE）、平均误差率（MER）、均方误差（MSE）和平均绝对误差（MAE）评价模型拟合及预测效果。结果 SARIMA（0,1,1）（0,1,1）₁₂为最优SARIMA模型。SARIMA模型、组合模型A和组合模型B拟合的MAPE 分别为15.724%、12.217%、13.941%,MER分别为15.168%、10.179%、14.042%,MSE分别为0.336、0.167、0.713,MAE分别为0.296、0.199、0.274。预测的MAPE 分别为12.069%、7.904%、9.598%,MER分别为12.331%、7.872%、10.636%,MSE分别为0.022、0.013、0.025,MAE分别为0.138、0.088、0.119。结论考虑时间因素的SARIMA-RBF组合模型为最优拟合及预测模型,具有良好推广应用价值。

关键词: SARIMA, RBF神经网络, 季节指数, 流行性腮腺炎

Abstract: Objective To explore the prospect of SARIMA-RBF combination model in fitting and predicting the epidemic tendency of mumps in China. Methods SARIMA model was established using the monthly incidence data of mumps from January 2005 to December 2015 in China. The fitted value based on the SARIMA model was taken as the input vector and the actual value was used as the output vector. Two SARIMA-RBF combination models were established according to the time factor as the input vector or not. The time factor was added as the combination model A, and the time factor was not added as the combination model B. Using SARIMA model and two SARIMA-RBF combination models, the incidence rates of mumps were predicted from July to December 2016, and compared with actual values. Three model fitting and prediction effects were evaluated by using mean absolute percentage error （MAPE）, mean error rate （MER）, mean square error （MSE）, and mean absolute error （MAE）. Results The SARIMA （0, 0, 1）（0, 1, 1）₁₂ was the optimal SARIMA model. Fitted by SARIMA model, combination model A and combination model B, MAPEs were 15. 724%, 12. 217%, and 13. 941%, the MERs were 15. 168%, 10. 179%, and 14. 042%, the MSEs were 0. 336, 0. 167, and 0. 713, and the MAEs were 0. 296, 0. 199, and 0. 274,respectively; the predicted MAPEs were 12. 069%, 7. 904%, and 9. 598%, the MERs were 12. 331%, 7. 872%, and 10. 636%, the MSEs were 0. 022, 0. 013, and 0. 025, and the MAEs were 0. 138, 0. 088, and 0. 119, respectively. Conclusion The SARIMA-RBF combination model considering time factor was the optimal fit and prediction model, with application value.

Key words: SARIMA, RBF neural network, Seasonal index, Mumps

中图分类号:

R183.3

李军, 刘霞, 高礼华. SARIMA-RBF组合模型在流行性腮腺炎流行趋势预测中的应用[J]. 华南预防医学, 2020, 46(3): 214-218.

LI Jun, LIU Xia, GAO Li-hua. Application of SARIMA-RBF combination model in predicting incidence tendency of mumps[J]. South China Journal of Preventive Medicine, 2020, 46(3): 214-218.

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