"auto.arima" calculates a model that cannot predict
<p>I applied the <code>auto.arima</code> function from the <code>forecast</code> package to find the best fitting model <code>mdl_1</code> for the time series <code>x</code>:</p>

<pre><code>library(forecast)

x &lt;- c(437, 403, 390, 398, 401, 396, 420, 472, 501, 508, 495, 488, 
       458, 484, 434, 430, 469, 431, 403, 391, 401, 427, 424, 445, 449, 
       429, 429, 413, 417, 390, 375, 438, 435, 446, 448, 452, 436, 438, 
       470, 437, 428, 406, 409, 416, 439, 440, 498, 527, 481, 461, 431, 
       405, 398, 409, 425, 475, 503, 531, 510, 484, 490, 455, 468, 438, 
       469, 527, 489, 448, 448, 483, 547, 524)

mdl_1 &lt;- auto.arima(x)
mdl_2 &lt;- Arima( x, order=c(2,1,2), include.drift=FALSE )
</code></pre>

<p>Here is the result:</p>

<pre><code>&gt; mdl_1
Series: x 
ARIMA(2,1,2) with drift         

Coefficients:
         ar1      ar2      ma1     ma2   drift
      1.5191  -0.7501  -1.6225  0.6780  0.7976
s.e.  0.1773   0.1401   0.2060  0.2075  0.7621

sigma^2 estimated as 597.7:  log likelihood=-328.43
AIC=668.87   AICc=670.18   BIC=682.44
</code></pre>

<p><code>mdl_2</code> is of the same type, but doesn't have a drift term:</p>

<pre><code>&gt; mdl_2
Series: x 
ARIMA(2,1,2)                    

Coefficients:
         ar1     ar2      ma1      ma2
      0.0134  0.4741  -0.0278  -0.8807
s.e.  0.2623  0.2519   0.2112   0.2079

sigma^2 estimated as 628.9:  log likelihood=-330.2
AIC=670.4   AICc=671.32   BIC=681.71
</code></pre>

<p><code>mdl_2</code> can predict, but <code>mdl_1</code> cannot:</p>

<pre><code>&gt; predict(mdl_2)
$pred
Time Series:
Start = 73 
End = 73 
Frequency = 1 
[1] 495.5033

$se
Time Series:
Start = 73 
End = 73 
Frequency = 1 
[1] 25.07916

&gt; predict(mdl_1)
Error in array(x, c(length(x), 1L), if (!is.null(names(x))) list(names(x),  : 
  'data' must be of a vector type, was 'NULL'
&gt; 
</code></pre>

<p>Where does this error message come from?</p>