| ARIMA模型与GRNN模型对肺结核发病率预测的对比研究 |
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| 引用本文： | 胡晓媛,吴娟,孙庆文,沙琨,王玲玲,李敏.ARIMA模型与GRNN模型对肺结核发病率预测的对比研究[J].第二军医大学学报,2016,37(1):115-119. |
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| 作者姓名： | 胡晓媛 吴娟 孙庆文 沙琨 王玲玲 李敏 |
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| 作者单位： | 1. 第二军医大学海军医学系航海特殊损伤防护教研室,上海,200433;2. 成都军区总医院药剂科,成都,610083;3. 第二军医大学基础部数理教研室,上海,200433;4. 第二军医大学训练部信息化办公室,上海,200433;5. 解放军309医院全军结核病研究所,北京,100091 |
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| 基金项目： | 中国博士后科学基金资助项目（2013M542491） |
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| 摘 要： | 目的 比较自回归移动平均(ARIMA)模型与广义回归神经网络(GRNN)模型对于肺结核发病率的预测性能.方法 根据我国2004年1月至2012年12月的肺结核逐月发病率数据资料,应用Eviews 7.0.0.1建立ARIMA模型,应用Matlab 7.1的神经网络工具箱建立GRNN模型;选取2013年肺结核逐月发病率数据对两种预测模型进行检验,比较预测结果.结果 ARIMA模型和GRNN模型的Theil不等系数(TIC)分别是0.034和0.059,说明ARIMA模型对我国2013年肺结核逐月发病率的拟合程度优于GRNN模型,ARIMA模型相对误差绝对值仅为GRNN模型的57.19％.结论 ARIMA预测模型更适合用于我国肺结核发病率的预测;建议尝试组合模型预测肺结核发病率.
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| 关 键 词： | 回归移动平均模型 广义回归神经网络模型 肺结核 预测 |
| 收稿时间： | 2015/4/28 0:00:00 |
| 修稿时间： | 9/5/2015 12:00:00 AM |
Comparative study on ARIMA model and GRNN model for predicting the incidence of tuberculosis |
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| HU Xiao-yuan,WU Juan,SUN Qing-wen,SHA Kun,WANG Ling-ling and LI Min.Comparative study on ARIMA model and GRNN model for predicting the incidence of tuberculosis[J].Academic Journal of Second Military Medical University,2016,37(1):115-119. |
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| Authors: | HU Xiao-yuan WU Juan SUN Qing-wen SHA Kun WANG Ling-ling and LI Min |
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| Institution: | 1. Department of Navigation Special Damage Protection, Faculty of Naval Medicine, Second Military Medical University, Shanghai 200433, China;2. Department of Pharmacy, General Hospital, PLA Chengdu Military Area Command, Chengdu 610083, Sichuan, China;3. Department of Mathematics & Physics, College of Basic Sciences, Second Military Medical University, Shanghai 200433, China;4. Office of Informatization, Division of Training, Second Military Medical University, Shanghai 200433, China;5. Institute for Tuberculosis Research, No. 309 Hospital of PLA, Beijing 100091, China*Corresponding author. |
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| Abstract: | Objective : To compare the performance of ARIMA model and GRNN model for predicting the incidence of tuberculosis. Methods: Set up ARIMA model by Eviews7.0.0.1 and GRNN model by neural network toolbox of Matlab7.1 according to monthly tuberculosis incidence data from January 2004 to December 2012 in China. Compare prediction of 2013 monthly tuberculosis incidence data with both models. Results: The TIC are 0.034 and 0.059 for ARIMA model and GRNN model respectively, indicating that ARIMA model is better than GRNN model to fit with 2013 the monthly incidence of tuberculosis. The absolute value of the relative error for ARIMA model is only 57.19% of GRNN model. Conclusion : ARIMA prediction model is more suitable for the incidence of TB. We suggest to try a combination of models predict the incidence of infectious diseases. |
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| Keywords: | ARIMA model GRNN model the incidence of tuberculosis prediction |
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