I was reading about different variants of backtesting in time series- expanding window & rolling window. I could find in texts about when to use which, but still I'm sort of unanswered.
Here's what I've read- Expanding (recursive) window is useful when the series has a strong seasonal pattern and stable trend as in this case the first observations of the series contain potential information the future values. While rolling window is useful when we have a rather volatile series or when the most recent history is more relevant for forecasting (high correlation with most recent lags).
While this is fine, I'm still not able to clearly understand why the following is suggested as decision criteria. What exactly is the need to define two different structures of windows? What if we use expanding window to estimate the forecasting error for a volatile series?
Because, as far as I know, we anyway train our final model on the whole data that's available. Then what's the point using a rolling window because of the reasoning that's given.