Trying to "understand" a time series' patterns it is intuitively tempting to use STL decomposition as the concept of distinguishing between trend, season and the rest makes sense.
But my experience tells me that no static algorithm will lead under all circumstances to useful results.
So my general question/s is/are when should you not apply STL decomposition and if you do, what observation in the STL result might in you experience indicate a faulty/useless decomposition?
Like you wouldn't blindly trust a correlation analysis of two variables without having a look at a scatter plot, b/c outliers might lead to a high correlation coefficient indicating a non-existant relation.
I'm a newbie in this area, so a more extensive answer would be great.