6 research outputs found
Comparison of Two Hybrid Models for Forecasting the Incidence of Hemorrhagic Fever with Renal Syndrome in Jiangsu Province, China
<div><p>Background</p><p>Cases of hemorrhagic fever with renal syndrome (HFRS) are widely distributed in eastern Asia, especially in China, Russia, and Korea. It is proved to be a difficult task to eliminate HFRS completely because of the diverse animal reservoirs and effects of global warming. Reliable forecasting is useful for the prevention and control of HFRS.</p><p>Methods</p><p>Two hybrid models, one composed of nonlinear autoregressive neural network (NARNN) and autoregressive integrated moving average (ARIMA) the other composed of generalized regression neural network (GRNN) and ARIMA were constructed to predict the incidence of HFRS in the future one year. Performances of the two hybrid models were compared with ARIMA model.</p><p>Results</p><p>The ARIMA, ARIMA-NARNN ARIMA-GRNN model fitted and predicted the seasonal fluctuation well. Among the three models, the mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of ARIMA-NARNN hybrid model was the lowest both in modeling stage and forecasting stage. As for the ARIMA-GRNN hybrid model, the MSE, MAE and MAPE of modeling performance and the MSE and MAE of forecasting performance were less than the ARIMA model, but the MAPE of forecasting performance did not improve.</p><p>Conclusion</p><p>Developing and applying the ARIMA-NARNN hybrid model is an effective method to make us better understand the epidemic characteristics of HFRS and could be helpful to the prevention and control of HFRS.</p></div
The observed HFRS incidence and modeling and forecasting values simulated by ARIMA, ARIMA-NARNN and ARIMA-GRNN model.
<p>The data set was divided into modeling and forecasting data set with a vertical line; the left is the modeling stage, and the right is the forecasting stage.</p
Parameter estimates and their testing resulting of the final seasonal ARIMA (0,1,1)×(0,1,1)<sub>12</sub> model.
<p>Parameter estimates and their testing resulting of the final seasonal ARIMA (0,1,1)×(0,1,1)<sub>12</sub> model.</p
Monthly HFRS incidence series of Jiangsu province in China from January 2004 to December 2012.
<p>Monthly HFRS incidence series of Jiangsu province in China from January 2004 to December 2012.</p
The time series response plot of target series.
<p>This plot displays the inputs, targets and errors versus time. In the training, validation and testing samples the errors were small. We assured that the model was suitable.</p
The selection of the optimal spread of the ARIMA-GRNN hybrid model.
<p>When the spread was 0.006, the RMSE for the testing samples is the least.</p