Hybrid methodology for tuberculosis incidence time-series forecasting based on ARIMA and a NAR neural network
Hybrid methodology for tuberculosis incidence time-series forecasting based on ARIMA and a NAR neural network
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DOI：
10.1017/s0950268816003216
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发表时间：
2017-04-01
影响因子：
4.2
通讯作者：
Wu, M. C.
中科院分区：
文献类型：
--
作者：
Wang, K. W.;Deng, C.;Wu, M. C.
结核病（TB）影响着全球人民，在中国被重新视为一个严重的公共卫生问题。可靠的预测对预防和控制结核病是有用的。本研究提出一个结合自回归整合移动平均（ARIMA）与非线性自回归（NAR）神经网络的混合模型，以预测2007年1月至2016年3月的结核病发病率。比较了混合模型和ARIMA模型的预测性能。将最佳拟合混合模型与ARIMA（3，1，0）x（0，1，1）12和NAR神经网络相结合，该网络具有4个延迟和隐藏层中的12个神经元。ARIMA-NAR混合模型在建模性能上的均方误差、平均绝对误差和平均绝对百分比误差分别为0.2209、0.1373和0.0406，与ARIMA模型相比，ARIMA-NAR混合模型可以产生更准确的结核病发病率预测。研究表明，ARIMA-NAR混合模型的建立和应用是一种有效的方法，可以拟合时间序列数据的线性和非线性模式，该模型可以为结核病的预防和控制提供帮助。
Tuberculosis (TB) affects people globally and is being reconsidered as a serious public health problem in China. Reliable forecasting is useful for the prevention and control of TB. This study proposes a hybrid model combining autoregressive integrated moving average (ARIMA) with a nonlinear autoregressive (NAR) neural network for forecasting the incidence of TB from January 2007 to March 2016. Prediction performance was compared between the hybrid model and the ARIMA model. The best-fit hybrid model was combined with an ARIMA (3,1,0) x (0,1,1) 12 and NAR neural network with four delays and 12 neurons in the hidden layer. The ARIMA-NAR hybrid model, which exhibited lower mean square error, mean absolute error, and mean absolute percentage error of 0.2209, 0.1373, and 0.0406, respectively, in the modelling performance, could produce more accurate forecasting of TB incidence compared to the ARIMA model. This study shows that developing and applying the ARIMA-NAR hybrid model is an effective method to fit the linear and nonlinear patterns of time-series data, and this model could be helpful in the prevention and control of TB.