When you have a time series that contains both trend and seasonal components, I learned that either seasonal decomposition (e.g., forecast the deseasonalized series, then add back the seasonal factor to obtain forecasts of the original series) or Holt-Winters methods (additive or multiplicative) can be used for forecasting (or just data fitting) purposes.
Is there a general rule (or merely observation) when seasonal decomposition should be preferred to HW? I personally feel like HW is easier to use, and more responsive to changes in later observations. I read that seasonal decomposition is useful for macroeconomic data. But since the seasonal factors in the decomposition are calculated from past data, and they are constant for each season index throughout the series (this is true for the basic decomposition method I read from a textbook, but not sure if it's true for other decomposition methods), I'm not sure how good it would be for forecasting purpose. Should seasonal decomposition be considered when trying to forecast a seasonal series? If so, for which type of series would it work best?