Seasonal adjustmentis a crucial step preprocessing the data for further research. Researcher however has a number of options for trend-cycle-seasonal decomposition. The most common (judging by the number of citations in empirical literature) rival seasonal decomposition methods are X-11(12)-ARIMA, Tramo/Seats (both implemented in Demetra+) and R‘s stl. Seeking to avoid random choice between the above-mentioned decomposition techniques (or other simple methods like seasonal dummy variables) I would like to know a basic strategy that leads to choosing seasonal decomposition method effectively.Several important subquestions (links to a discussion are welcome too) could be:

- What are the similarities and differences, strong and weak points of the methods? Are there any special cases when one method is more preferable than the others?
- Could you provide general guides to what is inside the black-box of different decomposition methods?
- Are there special tricks choosing the parameters for the methods (I am not always satisfied with the defaults,
`stl`

for example has many parameters to deal with, sometimes I feel I just don’t know how to choose these ones in a right way).- Is it possible to suggest some (statistical) criteria that the time series is seasonally adjusted efficiently (correlogram analysis, spectral density? small sample size criteria? robustness?).

**Answer**

If you are willing to learn to understand the diagnostics, X12-ARIMA provides a boatload of diagnostics that range from (ASCII) graphs to rule-of-thumb indicators. Learning and understanding the diagnostics is something of an education in time series and seasonal adjustment.

On the other hand, X12-ARIMA software is a one-trick pony, while using stl in R would allow you to do other things and to switch to other methods (decompose, dlm’s, etc) if you wish.

On the other-other hand, X12-Arima makes it easier to include exogenous variables and to indicate outliers, etc.

**Attribution***Source : Link , Question Author : Dmitrij Celov , Answer Author : Wayne*