ARIMA模型和Holt-Winters指数平滑法在自杀死亡中的预测效果比较

目的 探讨比较自回归求和滑动平均(autoregressive integrated moving average,ARIMA)模型和Holt-Winters指数平滑法在自杀死亡预测中的应用.方法 利用河北省2014年1月-2018年6月自杀月度死亡资料分别建立ARIMA模型和Holt-Winters指数平滑模型,对2...

Comparison of effects of ARIMA model and Holt-Winters exponential smoothing method in the prediction of suicide death

Objective To explore and compare the application of autoregressive integrated moving average (ARIMA) model and Holt-Winters exponential smoothing method in the prediction of suicide death. Methods The ARIMA model and the Holt-Winters exponential smoothing model were established respectively based on the data about the monthly death cases of suicide from January 2014 to June 2018 in Hebei Province. The monthly death cases of suicide from July to December 2018 were predicted and then compared with the actual number of deaths. And the prediction effects of the two models were evaluated based on root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). Results A total of 2,882 suicide deaths were reported in Hebei Province from 2014 to 2018, and the overall level of suicide deaths showed a downward trend. The best ARIMA model established was ARIMA (0,1,1)(1,1,0)₁₂, and RMSE, MAE and MAPE of the predicted results were 5.99, 4.67 and 9.80%, respectively. The best fitting model of Holt-Winters exponential smoothing method was the multiplicative model, and RMSE, MAE and MAPE of the predicted results were 6.03, 5.17 and 11.44%, respectively. Conclusion The prediction effect of ARIMA model is superior to that of Holt-Winters exponential smoothing method, and it is more suitable for short-term prediction of trend of suicide deaths.