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- 2018
基于ARIMA模型预测梅毒月发病率的价值
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Abstract:
摘要：目的 探讨建立ARIMA模型在梅毒月发病率预测中的应用价值，为梅毒防控工作提供依据。方法 运用Eviews8.0软件对2009年1月-2015年12月我国梅毒月发病率数据建立ARIMA模型，利用2016年1月-6月实际数据验证，评价模型精度指标采用均方根误差(root mean squared error, RMSE)、平均绝对误差(mean absolute error, MAE)、平均绝对百分误差(mean absolute percentage error, MAPE)、平均相对误差(mean relative error, MRE)。同法外推预测2016年7月-12月全国梅毒月发病率。结果 2009年1月-2016年6月全国梅毒月发病率最优模型是ARIMA(2,1,1)×(0,1,1)12，模型表达式为：(1-B)(1-B12)(1+0.820B) (1+0.566B2) x2t=(1+0.365B) (1+0.897B12)εt，R2=0.832，RMSE=0.181，MAE=0.118，MAPE=5.088。外推2016年7月-12月预测结果分别为3.124、3.008、2.906、2.691、2.714、2.717。结论 ARIMA模型具有较高的预测精度，可较好地拟合我国梅毒月发病率的演变趋势并进行短期预测。
ABSTRACT: Objective To explore the value of the autoregressive integrated moving average model (ARIMA) applied to predict monthly incidence of syphilis so as to provide basis for prevention and control of syphilis. Methods Eviews 8.0 was used to establish the ARIMA model based on the data of monthly incidence of syphilis in China from January 2009 to December 2015. Then the data of the first half of 2016 were used to verify the predicted results. The predictions were evaluated by RMSE, MAE, MAPE and MRE models. Then the monthly incidence of syphilis in the second half of 2016 was predicted. Results The optimal model for the monthly incidence of syphilis from January 2009 to June 2016 was the model of ARIMA (2,1,1)×(0,1,1)12, its equation was (1-B)(1-B12) (1+0.820B)(1+0.566B2)x2t=(1+0.365B)(1+0.897B12)εt, its parameters are as follows: R2=0.832, RMSE=0.181, MAE=0.118, MAPE=5.088. The predicted monthly incidence values (10-5) of the second half of 2016 were 3.124, 3.008, 2.906, 2.691, 2.714, and 2.717. Conclusion ARIMA model has a relatively good prediction precision. Therefore, it can make short-term prediction based on the evolution trend of monthly incidence of syphilis in China
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