I'm trying to forecast a seasonal time series based on its historical values, and also two more time series (that are seasonal themselves.)
I'm trying to use an
auto.arima, and I'm going to input the other two time series (the exogeneous regressors) as a contatenated list of dummy variables, in auto.arima's
I am having difficulty how to use the forecast function after this point. I've written up the following code, but I don't understand what I should put in the
newxreg parameters of the forecast function.
tempfit<-auto.arima(dnew, xreg=dExt) plot(forecast(tempfit, xreg=dnew1,newxreg=dExt1))
Also, my data points for these three series were all values per day that had a seven day seasonality. In order to let auto.arima calculate the (p,q,d) for seasonality, I converted them to time series with a frequency of 7. Now, after forecasting is done, the plot shows one unit for every seven days. How can I covert this back to one unit per day?
Further, do you happen to know how we can input a set of external regressors to an ETS model?
I just saw the following page from Dr. Hyndman: Time series modeling with dynamic regressors in SAS vs R
Is it safe to assume that I don't need to enter a
newxreg parameter for my forecast?
Also, I want to know if it's statistically correct to use the two external regressors in
xreg, but then also use a number of dummy variables in
xreg that will represent the seasonality of these two variables.