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 | ||