I'm newbie to forecast and working on a time series. I begin with splitting data (lets assume there are 100 daily observations) into train set (first 70 days) and test set(last 30 days), then fit Holt Winter and ARIMA model with train set.
Q1: I think the next step is to use trained models to predict day 71-100 and compare with actual data from test set. Which is the correct way to predict day 71-100? (or maybe both are wrong?) A. use trained model + train set to do 30-steps forecast B. use trained model + train set + test set to do 1-step forecast (if this is correct way a code snippet would be very helpful)
Q2: The business goal is to forecast N days, should I take N into consideration during model training/testing/selection process?
Q3: The time series has strong 7-day seasonality, does this effect train/test splitting? i.e, is it preferable to split on full cycle rather than on middle of a cycle?