Choosing seasonal decomposition method

Seasonal adjustment is 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:

  1. 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?
  2. Could you provide general guides to what is inside the black-box of different decomposition methods?
  3. 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).
  4. 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?).


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.

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

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