Creating an ARIMA model

After determining the optimum p, d, and q parameters for an ARIMA model, we can now create an ARIMA model with the Arima function.

Getting ready

Ensure you have completed the previous recipe by generating a time series object and storing it in a variable, ts.sim.

How to do it…

Please perform the following steps to build an ARIMA model:

1. First, we can create an ARIMA model with time series ts.sim, with parameters p=1, d=1, q=0:

   > library(forecast)
   > fit <- Arima(ts.sim, order=c(1,1,0))
   > fit
   Series: ts.sim 
   ARIMA(1,1,0)                    
   
   Coefficients:
            ar1
         0.7128
   s.e.  0.0685
   
   sigma^2 estimated as 0.7603:  log likelihood=-128.04
   AIC=260.09   AICc=260.21   BIC=265.3
   

2. Next, use the accuracy function to print the training set errors of the model:

   > accuracy(fit)
                         ME     RMSE       MAE       MPE
   Training set 0.004938457 0.863265 0.6849681 -41.98798
                    MAPE      MASE          ACF1
   Training set 102.2542 0.7038325...