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Table 1 The fitting RMSEs, the fitting MAPEs, the forecast RMSEs, and the forecast MAPEs from models (with raw data from 2013 to 2018)

From: Collateral effects of COVID-19 countermeasures on hepatitis E incidence pattern: a case study of china based on time series models

 

Model

SARIMA

Holt-Winters

NNAR

Fitting with raw data from 2013 to 2018

RMSE

206.23

198.63

189.58

MAPE

6.03%

6.62%

6.26%

Forecasts from Jan. to Dec. 2019

RMSE

169.24

165.99

172.36

MAPE

5.46%

5.44%

5.32%

Forecasts from Jan. to Nov. 2019

RMSE

114.37

115.59

123.28

MAPE

3.80%

3.87%

3.71%

  1. MAPE mean absolute percentage error, RMSE root mean square error