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Table 3 Comparison of SARIMA models with covariate

From: Time series analysis of mumps and meteorological factors in Beijing, China

Model Meteorological factors AIC
Variables Lag β SE(β) T P-value
SARIMA(1,1,1)(0,1,1)12 T 0 0.016 0.007 2.377 0.018* -67.58**
SARIMA(1,1,1)(0,1,1)12 W 5 0.042 0.037 1.115 0.266 -63.20
SARIMA(1,1,1)(0,1,1)12 W 6 -0.073 0.037 1.952 0.052 -65.75**
SARIMA(1,1,1)(0,1,1)12 RH 0 -0.001 0.001 1.000 0.318 -62.95
SARIMA(1,1,1)(0,1,1)12 RH 1 0.002 0.001 1.769 0.078 -64.85**
SARIMA(1,1,1)(0,1,1)12 RH 2 -0.002 0.001 1.769 0.078 -64.92**
SARIMA(1,1,1)(0,1,1)12 RH 5 -0.001 0.001 0.429 0.669 -62.16
SARIMA(1,1,1)(0,1,1)12 V 1 0.005 0.009 0.517 0.606 -62.22
SARIMA(1,1,1)(0,1,1)12 V 2 -0.018 0.009 2.023 0.044* -66.01**
SARIMA(1,1,1)(0,1,1)12 V 4 0.016 0.009 1.830 0.069 -65.20**
SARIMA(1,1,1)(0,1,1)12 V 6 -0.006 0.009 0.689 0.492 -62.43
SARIMA(1,1,1)(0,1,1)12 T 0 0.015 0.007 2.206 0.028* -65.56**
V 2 -0.016 0.009 1.862 0.064
  1. T: temperature; W: wind speed; RH: relative humidity; V: vapor pressure; *: P value < 0.05; **: AIC value < -63.96