I am using forecast() package in R to predict future values. I have a time series data for approx 6-7 years.
First, I split the data into training set and test set. Test set contained values of the last 12 months of original data. Then, applied ets() and auto.arima() on the training set. This can be done by iterating 12 times using 1-step ahead OR by running once to get 12 points at the same time, which is my question no. 1
Then, the output received is compared with the test set to check which of the two (ets or auto.arima) gives minimal error in comparison with test set. The selected model class is then applied on the original data set to predict future 12 values. So, here arises my question no. 2
In Short, I have two things to do
- Compare accuracy measures for test set of ets and auto.arima to select one
- Predict future values using the derived model from the first step
Should I use one-step ahead or 12-step ahead forecasts to generate data for test set?
Should I use one-step ahead or 12-step ahead forecasts to predict future values?
Should the method be same in both the steps mentioned above?