In the following example, how do I extract the models and corresponding AICcs as a data frame? For instance, first two rows would be:

model | AICc |
---|---|

ARIMA(2,0,2)(1,0,1)[12] with non-zero mean | 1003.3450 |

ARIMA(0,0,0) | 996.8378 |

```
library(forecast)
# Data
set.seed(123)
z <- ts(rnorm(120, 30, 17), start=2008, frequency=12)
# Auto ARIMA
auto.arima(z, trace = TRUE)
#>
#> ARIMA(2,0,2)(1,0,1)[12] with non-zero mean : 1003.345
#> ARIMA(0,0,0) with non-zero mean : 996.8378
#> ARIMA(1,0,0)(1,0,0)[12] with non-zero mean : 999.0416
#> ARIMA(0,0,1)(0,0,1)[12] with non-zero mean : 999.1399
#> ARIMA(0,0,0) with zero mean : 1187.768
#> ARIMA(0,0,0)(1,0,0)[12] with non-zero mean : 996.902
#> ARIMA(0,0,0)(0,0,1)[12] with non-zero mean : 997
#> ARIMA(0,0,0)(1,0,1)[12] with non-zero mean : 999.0153
#> ARIMA(1,0,0) with non-zero mean : 998.9257
#> ARIMA(0,0,1) with non-zero mean : 998.9227
#> ARIMA(1,0,1) with non-zero mean : 996.8584
#>
#> Best model: ARIMA(0,0,0) with non-zero mean
#> Series: z
#> ARIMA(0,0,0) with non-zero mean
#>
#> Coefficients:
#> mean
#> 30.2625
#> s.e. 1.3823
#>
#> sigma^2 estimated as 231.2: log likelihood=-496.37
#> AIC=996.74 AICc=996.84 BIC=1002.31
```

^{Created on 2020-11-22 by the reprex package (v0.3.0)}