SARIMA模型和Holt-Winters指数平滑法在江苏省肺结核发病数预测中的应用

郭在金; 龚浩; 周罗晶

doi:10.3784/jbjc.202201300027

SARIMA模型和Holt-Winters指数平滑法在江苏省肺结核发病数预测中的应用

Application of SARIMA model and Holt winters index smoothing method to predict incidence of pulmonary tuberculosis in Jiangsu

摘要

摘要:
目的建立季节性差分自回归移动平均（SARIMA）模型和Holt-Winters指数平滑法，对江苏省结核病发病数进行预测，并评价两种方法的准确性，旨在为江苏省肺结核防控提供科学参考。
方法利用2016年1月至2020年12月江苏省肺结核发病数据分别建立SARIMA模型和Holt-Winters指数平滑法模型，以2021年1—12月肺结核发病数验证模型并用均方根误差（RMSE）、平均绝对误差（MAE）、平均绝对百分比误差（MAPE）评价两种模型的预测效果。
结果拟合最佳的SARIMA模型为（0,1,2）（0,1,0）₁₂， RMSE为229.52， MAE为146.81， MAPE为6.33%，总的相对误差为5.21%。 Holt-Winters相加模型的RMSE为206.75，MAE为156.45，MAPE为6.63%，总的相对误差为7.74%。
结论两种模型均能较好的拟合肺结核发病数，SARIMA模型预测效果更佳。

Abstract:
Objective To establish a seasonal auto regressive integrated moving average (SARIMA) model and a Holt-Winters exponential smoothing model for the prediction of the case number of tuberculosis (TB) in Jiangsu province and provide scientific reference for the prevention and control of TB in Jiangsu.
Methods The SARIMA model and Holt-Winters exponential smoothing model were established by using the TB incidence data in Jiangsu from January 2016 to December 2020. The validation of the model used the TB incidence from January to December 2021 and evaluation of the models’ prediction effect used root-mean-square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE).
Results The best SARIMA model was (0,1,2) (0,1,0) ₁₂, the RMSE was 229.52, MAE was 146.81 and MAPE was 6.33%, and the total relative error was 5.21%. For Holt winters additive model, the RMSE was 206.75, MAE was 156.45, MAPE was 6.63%, and the total relative error was 7.74%.
Conclusion Both models can well fit the number of pulmonary TB, and the performance of SARIMA model was slightly better.

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