[R] Using the R predict function to forecast a model fit with auto.arima function

[PaulJr]

Hello R users,

Hope everyone is doing great. 

I have a dataset that is in .csv format and consists of two columns: one
named Period (which contains dates in the format yyyy_mm) and goes from
1995_10 to 2007_09 and the second column named pcumsdry which is a
volumetric measure and has been formatted as numeric without any commas or
decimals.

I imported the dataset as pauldataset and made use of the auto.arima
function which is great when you want R to suggest an appropriate arima
model to fit to your data.

I successfully got the arima fit by coding:

> paulfit<-auto.arima(pauldataset$pcumsdry)
> paulfit

which produced the following results:
Series: pauldataset$pcumsdry 
ARIMA(2,1,1) with drift         

Coefficients:
         ar1    ar2      ma1       drift
      0.2684  0.109  -0.8906  -15265.776
s.e.  0.1145  0.102   0.0798    8047.169

sigma^2 estimated as 2.869e+11:  log likelihood=-2265.02
AIC=4540.03   AICc=4540.43   BIC=4555.25

After this, I wanted to make some predictions based on the model suggested
by the auto.arima function. I tried to use the following code to get the
predictions:

variableA.pred<-predict(paulfit, n.ahead=36)
*Error in predict.Arima(paulfit, n.ahead = 36) : 
  'xreg' and 'newxreg' have different numbers of columns: 1 != 0*> 

Now, I have understood, and, according to my research the xreg comes into
play if you add external regressors to your arima fit, but since I am just
working with a univariate time series, I really do not understand the reason
for that error message.

Can anybody help me or tell me how could I do to be able to generate the
predictions?

Best regards,

Paul

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