Application of ARIMA-NAR combined model in predicting bacillary dysentery
10.16781/j.0258-879x.2017.10.1315
- Author:
Ke-Wei WANG
1
Author Information
1. Department of Epidemiologic and Health Statistics, Wuxi Medical College, Jiangnan University
- Publication Type:Journal Article
- Keywords:
Autoregressive integrated moving average model;
Bacterial dysentery;
Neural networks;
Nonlinear autoregressive model;
Prediction;
Time series
- From:
Academic Journal of Second Military Medical University
2017;38(10):1315-1320
- CountryChina
- Language:Chinese
-
Abstract:
Objective To explore the application of autoregressive integrated moving average (ARIMA) model, and ARIMA combined nonlinear autoregressive (ARIMA-NAR) model in predicting bacterial dysentery (BE) incidence. Methods Data of BE monthly incidences from Jan. 2004 to Feb. 2015 in Jiangsu Province were used as fitting samples, the 15-month data from Mar. 2015 to May 2016 were used in the prediction phase. ARIMA model and ARIMA-NAR model were established and the effects of two models were compared according to mean absolute error (MAE), mean square error (MSE), and mean absolute percentage error (MAPE), in which lower values suggested higher prediction accuracy. Results In the fitting phase, the MAE, MSE and MAPE of the ARIMA model were 0. 177 5, 0. 081 4 and 0. 184 7, respectively, while those of the ARIMA-NAR model were 0. 094 1, 0. 029 5 and 0. 104 6, respectively. In the prediction phase, the MAE, MSE and MAPE of the ARIMA model were significantly higher than those of the ARIMA-NAR model. Conclusion ARIMA-NAR combined model is superior to ARIMA model in predicting the time series of BE incidence in Jiangsu Province, suggesting that ARIMA-NAR model can be used to predict the incidence of BD.
