class_docstr: Conduct a KPSS test for stationarity.
  
  In econometrics, Kwiatkowski–Phillips–Schmidt–Shin (KPSS) tests are used
  for testing a null hypothesis that an observable time series is stationary
  around a deterministic trend (i.e. trend-stationary) against the
  alternative of a unit root.
  
  Parameters
  ----------
  alpha : float, optional (default=0.05)
      Level of the test
  
  null : str, optional (default='level')
      Whether to fit the linear model on the one vector, or an arange.
      If ``null`` is 'trend', a linear model is fit on an arange, if
      'level', it is fit on the one vector.
  
  lshort : bool, optional (default=True)
      Whether or not to truncate the ``l`` value in the C code.
  
  Notes
  -----
  This test is generally used indirectly via the
  :func:`pmdarima.arima.ndiffs` function, which computes the
  differencing term, ``d``.
  
  References
  ----------
  .. [1] R's tseries KPSS test source code: http://bit.ly/2eJP1IU
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code: def __init__(self, alpha=0.05, null="level", lshort=True):
      super(KPSSTest, self).__init__(alpha=alpha)
  
      self.null = null
      self.lshort = lshort
  
imports: from sklearn.base import BaseEstimator
  from sklearn.linear_model import LinearRegression
  from statsmodels import api
  from abc import ABCMeta, abstractmethod
  import numpy
  from ..compat.numpy import DTYPE
  from ..decorators import deprecated
  from ..utils.array import c, diff, check_endog
  from .approx import approx
  from ._arima import C_tseries_pp_sum
label_desc: Use image node_id 1 to create a new KPSSTest object from inherited base classes: _DifferencingStationarityTest with example: obj = KPSSTest(alpha, null, lshort)