Currently in our preprocessor module, the users can calculate trend and climatology after applying the rolling mean operation first, but they can not get the rolling mean data. For some use cases, the users may want to first apply rolling mean as a low pass filter and use the outputs for the rest of their experiment. So it is necessary to calculate rolling mean in some cases.
Although the calculation of rolling mean is fairly easy via xarray (simply one liner with data, see https://docs.xarray.dev/en/stable/generated/xarray.DataArray.rolling.html), we would like to include it in our pipeline (with fit and transform) as it allows the user to play with different parameters related to rolling mean easily.
This is the same for other built-in preprocessing approaches in xarray.
Currently in our preprocessor module, the users can calculate trend and climatology after applying the rolling mean operation first, but they can not get the rolling mean data. For some use cases, the users may want to first apply rolling mean as a low pass filter and use the outputs for the rest of their experiment. So it is necessary to calculate rolling mean in some cases.
Although the calculation of rolling mean is fairly easy via
xarray(simply one liner with data, see https://docs.xarray.dev/en/stable/generated/xarray.DataArray.rolling.html), we would like to include it in our pipeline (withfitandtransform) as it allows the user to play with different parameters related to rolling mean easily.This is the same for other built-in preprocessing approaches in
xarray.