class_docstr: Results class for seasonal decompositions
  
  Parameters
  ----------
  observed : array_like
      The data series that has been decomposed.
  seasonal : array_like
      The seasonal component of the data series.
  trend : array_like
      The trend component of the data series.
  resid : array_like
      The residual component of the data series.
  weights : array_like, optional
      The weights used to reduce outlier influence.
class_docstr_tok: ["Results","class","for","seasonal","decompositions","Parameters","--","--","--","--","--","observed",":","array_like","The","data","series","that","has","been","decomposed",".","seasonal",":","array_like","The","seasonal","component","of","the","data","series",".","trend",":","array_like","The","trend","component","of","the","data","series",".","resid",":","array_like","The","residual","component","of","the","data","series",".","weights",":","array_like",",","optional","The","weights","used","to","reduce","outlier","influence","."]
docstr: The weights used in the robust estimation
docstr_tok: ["The","weights","used","in","the","robust","estimation"]
code: def weights(self):
      """The weights used in the robust estimation"""
      return self._weights
  
imports: import numpy
  import pandas
  from pandas.core.nanops import nanmean
  from statsmodels.tools.validation import PandasWrapper, array_like
  from statsmodels.tsa.stl._stl import STL
  from statsmodels.tsa.filters.filtertools import convolution_filter
  from statsmodels.tsa.stl.mstl import MSTL
  from statsmodels.tsa.tsatools import freq_to_period
label_desc: Use image node_id 6 for calling the DecomposeResult obj's underlying member method code with example usage: obj.weights() and returns: self