Store auto.arima summary in multiple vectors
<p>I performed auto.arima function on multiple ts variables in a list </p>

<pre><code>arima_train &lt;- lapply(train_data, function(x) auto.arima(x$Value))
</code></pre>

<p>I can get the summaries of the function for all the variables </p>

<pre><code>&gt; for (i in (1:16)) summary(arima_train[[i]])
Series: x$Value 
ARIMA(0,1,0)                    

sigma^2 estimated as 2.808:  log likelihood=-137.4
AIC=276.81   AICc=276.86   BIC=279.07

Training set error measures:
                    ME     RMSE      MAE        MPE     MAPE      MASE      ACF1
Training set 0.0451375 1.664175 1.228471 0.04069765 2.268046 0.9866678 -0.188887
Series: x$Value 
ARIMA(0,0,0) with non-zero mean 

Coefficients:
      intercept
      5251.6806
s.e.   187.3747

sigma^2 estimated as 2563468:  log likelihood=-632.91
AIC=1269.81   AICc=1269.99   BIC=1274.37

Training set error measures:
                        ME     RMSE     MAE       MPE    MAPE      MASE       ACF1
Training set -2.829471e-12 1589.926 1012.06 -6.179073 17.3668 0.8272841 0.06356198
Series: x$Value 
ARIMA(1,1,0) with drift         

Coefficients:
         ar1   drift
      0.4006  0.3324
s.e.  0.1086  0.0907

sigma^2 estimated as 0.2205:  log likelihood=-46.04
AIC=98.08   AICc=98.44   BIC=104.87

Training set error measures:
                       ME      RMSE       MAE          MPE      MAPE     MASE       ACF1
Training set 0.0003775476 0.4597061 0.3308142 0.0007945521 0.1444236 0.669588 0.05640966
Series: x$Value 
ARIMA(0,1,0) with drift         

Coefficients:
        drift
      54.8873
s.e.  14.8586

sigma^2 estimated as 15900:  log likelihood=-443.67
AIC=891.34   AICc=891.51   BIC=895.86

Training set error measures:
                     ME     RMSE      MAE         MPE     MAPE      MASE        ACF1
Training set 0.07422375 124.3296 99.95529 -0.08126397 1.520543 0.9287823 -0.03885156
Series: x$Value 
ARIMA(0,2,1)                    

Coefficients:
          ma1
      -0.9171
s.e.   0.0565

sigma^2 estimated as 100261:  log likelihood=-502.59
AIC=1009.17   AICc=1009.35   BIC=1013.67

Training set error measures:
                   ME     RMSE      MAE        MPE      MAPE      MASE       ACF1
Training set 68.51967 309.9734 221.9339 0.04873783 0.1559967 0.9006235 -0.1312991
Series: x$Value 
ARIMA(1,2,1)                    

Coefficients:
         ar1      ma1
      0.2549  -0.9151
s.e.  0.1297   0.0538

sigma^2 estimated as 0.8075:  log likelihood=-91.49
AIC=188.98   AICc=189.34   BIC=195.72

Training set error measures:
                     ME      RMSE       MAE         MPE      MAPE      MASE        ACF1
Training set -0.1095264 0.8733085 0.6821252 -0.08248981 0.5255063 0.8663844 -0.02072697
Series: x$Value 
ARIMA(2,1,2)                    

Coefficients:
          ar1     ar2     ma1     ma2
      -0.1269  0.4314  0.7658  0.3584
s.e.   0.1816  0.1818  0.1826  0.1581

sigma^2 estimated as 0.04297:  log likelihood=12.48
AIC=-14.95   AICc=-14.03   BIC=-3.64

Training set error measures:
                      ME     RMSE       MAE      MPE     MAPE      MASE        ACF1
Training set 0.006785029 0.199965 0.1498849 1.903016 13.18623 0.6692976 -0.01844039
Series: x$Value 
ARIMA(0,2,2)                    

Coefficients:
          ma1      ma2
      -0.2629  -0.5857
s.e.   0.0920   0.0893

sigma^2 estimated as 1.257:  log likelihood=-106.99
AIC=219.97   AICc=220.33   BIC=226.72

Training set error measures:
                     ME     RMSE       MAE        MPE      MAPE      MASE       ACF1
Training set -0.1654204 1.089599 0.8857888 -0.1206975 0.6571573 0.8396662 0.08537194
Series: x$Value 
ARIMA(0,0,0) with non-zero mean 

Coefficients:
intercept  
     0.25  

sigma^2 estimated as 0:  log likelihood=Inf
AIC=-Inf   AICc=-Inf   BIC=-Inf

Training set error measures:
             ME RMSE MAE MPE MAPE MASE ACF1
Training set  0    0   0   0    0  NaN  NaN
Series: x$Value 
ARIMA(0,1,1)                    

Coefficients:
          ma1
      -0.3715
s.e.   0.1246

sigma^2 estimated as 877.4:  log likelihood=-340.9
AIC=685.8   AICc=685.97   BIC=690.32

Training set error measures:
                   ME     RMSE      MAE         MPE     MAPE      MASE       ACF1
Training set 1.621179 29.20693 21.85996 -0.05931622 5.767894 0.9993928 0.03373764
Series: x$Value 
ARIMA(1,2,1)                    

Coefficients:
         ar1      ma1
      0.2877  -0.9395
s.e.  0.1332   0.0528

sigma^2 estimated as 0.07365:  log likelihood=-7.22
AIC=20.43   AICc=20.82   BIC=27

Training set error measures:
                      ME      RMSE      MAE         MPE     MAPE     MASE        ACF1
Training set -0.02910269 0.2632887 0.193597 -0.02640935 0.181013 0.754128 -0.06465092
Series: x$Value 
ARIMA(0,0,0) with non-zero mean 

Coefficients:
      intercept
         0.3792
s.e.     0.0827

sigma^2 estimated as 0.4989:  log likelihood=-76.62
AIC=157.25   AICc=157.42   BIC=161.8

Training set error measures:
                        ME      RMSE      MAE  MPE MAPE      MASE        ACF1
Training set -3.251722e-14 0.7013751 0.512037 -Inf  Inf 0.7256413 -0.09038341
Series: x$Value 
ARIMA(0,1,0) with drift         

Coefficients:
         drift
      -88.7606
s.e.   28.2956

sigma^2 estimated as 57661:  log likelihood=-489.4
AIC=982.8   AICc=982.98   BIC=987.33

Training set error measures:
                    ME     RMSE      MAE         MPE     MAPE      MASE        ACF1
Training set 0.2040244 236.7675 186.6802 -0.07011135 1.570809 0.9429632 -0.08296941
Series: x$Value 
ARIMA(0,1,1) with drift         

Coefficients:
          ma1   drift
      -0.8659  0.0907
s.e.   0.1172  0.0137

sigma^2 estimated as 0.4673:  log likelihood=-73.42
AIC=152.83   AICc=153.19   BIC=159.62

Training set error measures:
                      ME      RMSE       MAE        MPE     MAPE      MASE      ACF1
Training set -0.04660173 0.6692201 0.4396176 -0.6847646 3.393219 0.9150644 0.1119422
Series: x$Value 
ARIMA(2,1,2) with drift         

Coefficients:
         ar1      ar2      ma1     ma2   drift
      1.3171  -0.8973  -1.2696  0.7378  0.1369
s.e.  0.0973   0.0889   0.1584  0.1495  0.0916

sigma^2 estimated as 0.9726:  log likelihood=-97.5
AIC=207.01   AICc=208.32   BIC=220.58

Training set error measures:
                       ME      RMSE      MAE      MPE     MAPE      MASE        ACF1
Training set -0.007610831 0.9441978 0.627121 23.63108 55.34784 0.9207112 -0.00091189
Series: x$Value 
ARIMA(1,0,0) with non-zero mean 

Coefficients:
         ar1  intercept
      0.3912     0.1423
s.e.  0.1076     0.0368

sigma^2 estimated as 0.03781:  log likelihood=16.67
AIC=-27.34   AICc=-26.99   BIC=-20.51

Training set error measures:
                      ME      RMSE       MAE  MPE MAPE      MASE       ACF1
Training set 0.000930651 0.1917348 0.1362479 -Inf  Inf 0.8794182 0.05445404
</code></pre>

<p>I want to create multiple vectors which should be able to save the following information for all the 16 variables</p>

<ol>
<li>Order </li>
<li>AICc value</li>
<li>Log Likelyhood (Fit) etc. </li>
</ol>

<p>Thank You. </p>