class_docstr: Simple Exponential Smoothing
  
  Parameters
  ----------
  endog : array_like
      The time series to model.
  initialization_method : str, optional
      Method for initialize the recursions. One of:
  
      * None
      * 'estimated'
      * 'heuristic'
      * 'legacy-heuristic'
      * 'known'
  
      None defaults to the pre-0.12 behavior where initial values
      are passed as part of ``fit``. If any of the other values are
      passed, then the initial values must also be set when constructing
      the model. If 'known' initialization is used, then `initial_level`
      must be passed, as well as `initial_trend` and `initial_seasonal` if
      applicable. Default is 'estimated'. "legacy-heuristic" uses the same
      values that were used in statsmodels 0.11 and earlier.
  initial_level : float, optional
      The initial level component. Required if estimation method is "known".
      If set using either "estimated" or "heuristic" this value is used.
      This allows one or more of the initial values to be set while
      deferring to the heuristic for others or estimating the unset
      parameters.
  
  See Also
  --------
  ExponentialSmoothing
      Exponential smoothing with trend and seasonal components.
  Holt
      Exponential smoothing with a trend component.
  
  Notes
  -----
  This is a full implementation of the simple exponential smoothing as
  per [1]_.  `SimpleExpSmoothing` is a restricted version of
  :class:`ExponentialSmoothing`.
  
  See the notebook `Exponential Smoothing
  <../examples/notebooks/generated/exponential_smoothing.html>`__
  for an overview.
  
  References
  ----------
  .. [1] Hyndman, Rob J., and George Athanasopoulos. Forecasting: principles
      and practice. OTexts, 2014.
code: def __init__(
      self,
      endog,
      initialization_method=None,  # Future: 'estimated',
      initial_level=None,
  ):
      super().__init__(
          endog,
          initialization_method=initialization_method,
          initial_level=initial_level,
      )
  
imports: from statsmodels.compat.pandas import deprecate_kwarg
  import contextlib
  from typing import Any
  from collections.abc import Hashable, Sequence
  import warnings
  import numpy
  import pandas
  from scipy.optimize import basinhopping, least_squares, minimize
  from scipy.special import inv_boxcox
  from scipy.stats import boxcox
  from statsmodels.tools.validation import array_like, bool_like, dict_like, float_like, int_like, string_like
  from statsmodels.tsa.base.tsa_model import TimeSeriesModel
  from statsmodels.tsa.exponential_smoothing.ets import _initialization_heuristic, _initialization_simple
  from statsmodels.tsa.holtwinters import _exponential_smoothers, _smoothers
  from statsmodels.tsa.holtwinters._exponential_smoothers import HoltWintersArgs
  from statsmodels.tsa.holtwinters._smoothers import to_restricted, to_unrestricted
  from statsmodels.tsa.holtwinters.results import HoltWintersResults, HoltWintersResultsWrapper
  from statsmodels.tsa.tsatools import freq_to_period
label_desc: Use image node_id 1 to create a new SimpleExpSmoothing object from inherited base classes: ExponentialSmoothing with example: obj = SimpleExpSmoothing(endog, initialization_method, initial_level)