class_docstr: Provides the base class for seasonal differencing tests such as the
  Canova-Hansen test and the Osborn-Chui-Smith-Birchenhall tests. These tests
  are used to determine the seasonal differencing term for a time-series.
class_docstr_tok: ["Provides","the","base","class","for","seasonal","differencing","tests","such","as","the","Canova-Hansen","test","and","the","Osborn-Chui-Smith-Birchenhall","tests",".","These","tests","are","used","to","determine","the","seasonal","differencing","term","for","a","time-series","."]
docstr: Estimate the seasonal differencing term.
  
  Parameters
  ----------
  x : array-like, shape=(n_samples,)
      The time series vector.
docstr_tok: ["Estimate","the","seasonal","differencing","term",".","Parameters","--","--","--","--","--","x",":","array-like",",","shape=","(","n_samples",",",")","The","time","series","vector","."]
code: def estimate_seasonal_differencing_term(self, x):
      """Estimate the seasonal differencing term.
  
      Parameters
      ----------
      x : array-like, shape=(n_samples,)
          The time series vector.
      """
  
imports: from collections import namedtuple
  import math
  from sklearn.linear_model import LinearRegression
  from sklearn.pipeline import make_pipeline
  from sklearn.preprocessing import StandardScaler
  from scipy.linalg import svd
  from statsmodels import api
  from statsmodels.tools import add_constant
  from abc import ABCMeta, abstractmethod
  from numpy.linalg import solve
  import numpy
  from .arima import _aicc
  from ..compat.numpy import DTYPE
  from .stationarity import _BaseStationarityTest
  from ..utils.array import c, diff, check_endog
  from ._arima import C_canova_hansen_sd_test
label_desc: Use image node_id 2 for calling the _SeasonalStationarityTest obj's underlying member method code with example usage: obj.estimate_seasonal_differencing_term(x) without return types