The Arima function in the forecast package can fit a regression model to the data with an ARIMA model for the errors. The `order` argument specifies the orders of the ARIMA model, while the argument `xreg` defines which data object contains the observations of the predictors. E.g., if `xreg` is a matrix of predictors:

    model = Arima(series, order = c(1,1,0), xreg = covariates)

To find the order of the ARIMA process, you can simply use the `auto.arima` function also found in the forecast package. It automatically locates the best-fitting ARIMA model to the data, “fit” defined by one of three possible information criteria in the `ic` argument: the AIC (given by `aic`), the AICc (`aicc`), or the BIC (`bic`). E.g.,

    model = auto.arima(series, ic = “aic”)

I think you may find [this][1] page really helpful, especially the section about R.


  [1]: http://this