STLDecomposition Implementation

Hello, First of all thank you for open sourcing the package; it is excellently written.

One potential issue I’ve noticed: the STL Decomposition seems to be implemented using a rolling window method for detrending data, not the Loess method. Here is a code snippet from transformer_1d.py:

def _remove_trend(self, s):
        s_trend = s.rolling(
            window=(self.freq_ if self.freq_ % 2 else self.freq_ + 1),
            center=True,
        ).mean()
        return s - s_trend

Perhaps I’m missing something? The current implementation seems to be what Hyndman calls Classical Decomposition, and not STL. There is a python library that does STL decompose and it is relatively lightweight. And if what I’m describing is indeed the behavior, can I work on implementing STL with Loess?

Any feedback would be greatly appreciated, Thank you

[tailaiw]

@yy910616 i just noticed that statsmodels v0.11 has been released.

[tailaiw]

@yy910616 I think you are right. I appreciate that you raised the problem. I’m gonna work on this and hopefully have it in the next release. Will keep you updated when a PR is ready to be reviewed.